{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python \t\t3.6\n",
      "Tensorflow \t1.0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras \t\t2.0.3\n"
     ]
    }
   ],
   "source": [
    "import sys; print('Python \\t\\t{0[0]}.{0[1]}'.format(sys.version_info))\n",
    "import tensorflow as tf; print('Tensorflow \\t{}'.format(tf.__version__))\n",
    "import keras; print('Keras \\t\\t{}'.format(keras.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ../mnist-data/train-images-idx3-ubyte.gz\n",
      "Extracting ../mnist-data/train-labels-idx1-ubyte.gz\n",
      "Extracting ../mnist-data/t10k-images-idx3-ubyte.gz\n",
      "Extracting ../mnist-data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"../mnist-data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55000, 784)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.train.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tfKxQBUuqZ1r1GEhEpv7KGDuDeNFFF9X4Ob8IwznnnAOkZuUBFi9e7Nr2PZM/ixS6PH+h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\nfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1199fd518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "for i in list(range(10)):\n",
    "    plt.subplot(1, 10, i+1)\n",
    "    pixels = mnist.test.images[i]\n",
    "    pixels = pixels.reshape((28, 28))\n",
    "    plt.imshow(pixels, cmap='gray_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Activation\n",
    "from keras.layers import Dropout, Flatten, Reshape\n",
    "from keras.layers import Convolution2D, MaxPooling2D\n",
    "from keras.layers import BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mlp(batch_normalization=False, activation='sigmoid'):\n",
    "    _in = Input(shape=(784,))\n",
    "    \n",
    "    for i in range(5):\n",
    "        x = Dense(128, activation=activation, input_shape=(784,))(x if i else _in)\n",
    "        if batch_normalization:\n",
    "            x = BatchNormalization()(x)\n",
    "\n",
    "    _out = Dense(10, activation='softmax')(x)\n",
    "    model = Model(_in, _out)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from  functools import reduce\n",
    "\n",
    "def print_layers(model):\n",
    "    for l in model.layers:\n",
    "        print(l.name, l.output_shape, [reduce(lambda x, y: x*y, w.shape) for w in l.get_weights()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import Callback\n",
    "\n",
    "class BatchLogger(Callback):\n",
    "    def on_train_begin(self, epoch, logs={}):\n",
    "        self.log_values = {}\n",
    "        for k in self.params['metrics']:\n",
    "            self.log_values[k] = []\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        for k in self.params['metrics']:\n",
    "            if k in logs:\n",
    "                self.log_values[k].append(logs[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sigmoid activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_1 (None, 784) []\n",
      "dense_1 (None, 128) [100352, 128]\n",
      "dense_2 (None, 128) [16384, 128]\n",
      "dense_3 (None, 128) [16384, 128]\n",
      "dense_4 (None, 128) [16384, 128]\n",
      "dense_5 (None, 128) [16384, 128]\n",
      "dense_6 (None, 10) [1280, 10]\n",
      "Train on 55000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "55000/55000 [==============================] - 2s - loss: 1.1213 - acc: 0.6402 - val_loss: 0.3869 - val_acc: 0.8936\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1239ab5f8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see http://cs231n.github.io/neural-networks-3/\n",
    "\n",
    "model = mlp(False, 'sigmoid')\n",
    "print_layers(model)\n",
    "\n",
    "bl_noBN = BatchLogger()\n",
    "\n",
    "from keras.optimizers import Adam\n",
    "model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(mnist.train.images, mnist.train.labels,\n",
    "          batch_size=128, epochs=1, verbose=1, callbacks=[bl_noBN],\n",
    "          validation_data=(mnist.test.images, mnist.test.labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_2 (None, 784) []\n",
      "dense_7 (None, 128) [100352, 128]\n",
      "batch_normalization_1 (None, 128) [128, 128, 128, 128]\n",
      "dense_8 (None, 128) [16384, 128]\n",
      "batch_normalization_2 (None, 128) [128, 128, 128, 128]\n",
      "dense_9 (None, 128) [16384, 128]\n",
      "batch_normalization_3 (None, 128) [128, 128, 128, 128]\n",
      "dense_10 (None, 128) [16384, 128]\n",
      "batch_normalization_4 (None, 128) [128, 128, 128, 128]\n",
      "dense_11 (None, 128) [16384, 128]\n",
      "batch_normalization_5 (None, 128) [128, 128, 128, 128]\n",
      "dense_12 (None, 10) [1280, 10]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/local/conda/lib/python3.6/site-packages/ipykernel_launcher.py:13: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 55000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "55000/55000 [==============================] - 6s - loss: 0.3140 - acc: 0.9042 - val_loss: 0.2629 - val_acc: 0.9284\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11b44fa90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see http://cs231n.github.io/neural-networks-3/\n",
    "\n",
    "model = mlp(True, 'sigmoid')\n",
    "print_layers(model)\n",
    "\n",
    "bl_BN = BatchLogger()\n",
    "\n",
    "from keras.optimizers import Adam\n",
    "model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(mnist.train.images, mnist.train.labels,\n",
    "          batch_size=128, epochs=1, verbose=1, callbacks=[bl_BN],\n",
    "          validation_data=(mnist.test.images, mnist.test.labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S62Owdrlh1ziArtMzzxhLni2Gqira7PiqP/5wtvuoo2h61lnAffd575PbsGSJ\nc7ltpWF3PcAICP7ONm8mYbd3rxFtAMW0paaSkGLBzvF6zzxDsWc2I0eS22NWFlmXAO/ryi6LLNrv\nuou+G4CEhi3abLQm4QcYQcEWUMA/WyOLFbd7JEDWWDecsbSwkM6zVy9jaUtNNe6Lycl0PUeNosyo\nbPXigUXus3foUPni8ePHO5OMbNhA14MHB0JZ+TIyKDZx0iTTrrvvpnPp1w9P4FYcBC3f2GogMpCF\nZiAhzKKt4jZu08bstz6JNgD7AQzVWh8HIBPA6Uqp/q51rgKQo7U+GsBUAI9VbzM9ENEmCMJhRHR0\nNDIyMvDaa69h4MCBGDx4MObMmYP169eja9euIbePtUYvIyMjUequbeMiMTERQ4YMwQ8//OCYP3To\nUBQXF2MhZyATDh8qK9o4mQQQvovkTTc5LVrBOHgQGDeOOl6A05rGnW7btc8WSr/+StaA/r7uDFtw\nli8nS9Qdd5hOpVfyCxZFdsf4mWeoc+ruINuijdvjJWLsTrw7lovFW6dOtO0llxgRtn49nTsnbcjP\nN8ts0caZC73OKSWF3Ox+/DF4HBkLgo4d6Xu67DLnurbFMVDcmE3Xrs57g61K7pT7gNNqCBjRkJBg\n2jVoEE3ZPZZFGJ9Tp07kPmi7JnpZEvk7O+ccY321RVtJiRE57nIGzZoZKyDz2WdUq277djo+EDip\nSlKS6dumpRmLVtOm/q5/zIEDRoiymLQHG/LySFjx/c73qNvSBpBb4oQJ5F55/fUkwPj6sfWud29z\n/Zs3N6KN28pwG4YNc37m78tyv8eECaGzvNqZLgG6Pj/+aEpFMDNnUhZO20JtW8dY6NnHt5i/MwPN\nsRcXgeI110aTe2bFz9b+7+vWLXibq5GoUCtoCnzgZkb7Xm55PBLAA773HwB4WimldKigiUNBRJsg\nCHVFZbLnVSODBw/G448/jldffRU9evTAbbfdhj59+kBxR8dHUlISCoLFkIRBaWkpFi1ahJtvvtlv\n2b333ovrr78eHTp0OKRjCA0Mt2gL5fJYWdEWrMjtjh3kbjV1qukQz5zpXIcFUXq6sdDYQi4/nzq2\nCxaYWk65uWQZ8qX5dgiU7dvJ9cvLIsKdXncMXFSUf6KG7GzqMGZlkcD64APg1FPN8tRUWsc+jle2\nx2bNyL3s11+po2h3cG2XZbtNdpycLdpGjvTfd8uW1Dk/5xxy7+SOuN2u7t3pPE48keJ63C6y69bR\nvktKnO4NwskUAAAgAElEQVSehYUU9+W2eHXpYmLgysuDC2V3BlzueLtF26JFzsQj0dHmXv3kE0rA\n4vIg8GPWLMpyyBkOARJttmhq3pymLBbY0ta0qbcV6eOPaX7fvnSfBLJgJiYakdSqlbEgud0jA/D1\nwqYYnprq7xI8ahTFbfL3ym3n8+BpRITJ+Hr55XRvvf8+fWbVkplpXAzLy4310LJ25eYCkXnlSAL8\nLW38fWVkGNdN3zF/+IG+Qs9yd3xMJi0NOO44etmMGAGtyRPygTYfImZ7Fj5c0h5qvy8XiSXatm8n\nr09m9Wpg7tYOuBnAJNyLP5OPxqIYan9ODuVhuvZaoGKo1G1VrUHCimlTSkUqpVYA2A3gG621Wwq3\nAbAVALTWpQDyADSvzob6IaJNEITDjMGDB2Pnzp0YMGAAWrZsibi4OE/XyObNm+PEE09E9+7dKxKR\nhAvHtPXs2RM9evTA+eyaYzFixAi0aNGiyuchNFDcos0WZV5UVrS5LVQ2559PcWW25YgFg1LUGeZO\naocO5IpWXOzsuOblkevlgw86RdGVV3pnMOT22J3rsjI6VqDsejExRoQAJF6ys6lNAHDzzWSRYzfE\nXr2M9SKUaMvMpA59QYERBfffT1PbrZFrknGcGvPtt+a9W2w2a2Y68199Zdy1Cwqc1soWLajXeuml\n9Nmdqe/gQRMbZccqFRRQh/6tt5yioX17kwY/VFbM3Fy6vk8+Se6rtmhLTyer64UXkttkVhZdo9xc\npyUkLY1KDTAnnuh/HC6kvWyZMy7PLdoCWdqSkpzbMWyx69iR9hNItCUlGZGZlmYGKWJi/EWbR/tv\nuqepaZsd69e5s78LYWIiDQRcdZW/KGZOPpmWc8KQXr3ofnnuORKCQ4YAxx9P94Q1GDF5MnBayadY\n1f9qEtNXXEEZGgGHaLso6kNMwj0VxtE33nAmHnUQ5bI1ue8/i7w88pwemvcRMGYMLr6/c4WnqS3a\nrrrKGS63YAGQBdpvCnKxsN0oREapirY98QSNH+Htt6mhXjF0NURISxsAaK3LAGQqpZIBfKSU6q61\nXhVqOzdKqWsBXAsA7Q9VmYpoEwThMGPYsGE4aFk3fufOmQ87Tu1tV00au6ba0+6Abh+v2dnGXHxn\np18GsJSL3wqHD+GItldeoU7z3LnOTiD3yLx44gmKz2IhxJ1m5uBBY1WyLcgsqrjYsS3aZs8my53b\n0rZxIwk6L4GQkOBMxb90KcUJ2Z3r8ePplZdH7XTHu0VHOwVqTg61gzuXvD5bkj78kARHUpK/e2Sz\nZk7BNHYsCbLCQhKCY8ZQkeYXX3RmkGSLWteulIY9Pp6Oa7tyumnaFDj6aPO5rIxifzhOiklMBP71\nL/PZqy/XvDkJDHdMW8uW1LFv2dJ07tPS6DtlE0YoPv/cxKCxtSw6mvqEHEvHVs3776fz6N2bRGpc\nnFOcAnSfuoVAbCxdr//8h64Ji5W4ODqv5s3pO/USbQkJtD8v0caW5IwMOu9p0yizoesafzYnESMO\nlCISoGvF/V2t/UXbo4/ij3ueQst5H1TMykMz07auXY0o7t3b31KVlIR33o1Au3EvY1AfeBMRQQlP\nrrmGLKkXXmjOg8s9AH4JccrLgUXoj5nn9kf3KFQMLHz/PbDzpwxcDGDBrgx83/w8vP/HeSh5lW65\nffv8/2p4f7+vi0QXe6b9Xbrgv4r5hcfhkWPfhCMgwBJt7nrvGzcCWyMzAJ+8WB3bp8Jrl2+5li1B\nLsq1TKXkodY6F8AcAKe7Fm0H0A4AlFJRAJoB8PNz0Fq/qLXuq7Xue8ijtCLaBEEQBKH2cPek3MJn\n+3ZKYnDaadT74gQhkZGBLW379lFnPTPTxCy5s8fZVjCONSoro4449wXsWmxs1crO9hdt3A53dkCA\nBJstXDguzHbVe+EFSvMNGJcvO34pJoZ6fdwp3LSJ2up2JWb3TXatS0tzHmf/fnIdZIYPp+K/SUm0\nbOdO0zFn1zRm2TJn0ormzf3Tqrtp2pQ655zx8scfKZEE12XkY7lT2UdHU0IXXzmSinWTk0mIsUWQ\nE2wAzuvFbZw+3T/johd2TBNfY/d92bYt3TP8PWVmmmMp5ezoR0aSxYSFCEDi4+yz6T62Y574vuQ2\nu6/Jzp0mgYh9joMGkZsh07o1WSwB8rNzxeq9OD0JNya/Q3FgaWnGklNWRgW2L7yQ3FdPOgno2xdF\nUU0d2+ejKa0HkGhj2rc37WMSE3HppRUZ74Mzfjy5Fd9wQxgrGy3sDp8++WTgkoltsK7bubh38TkV\niV55TGHfPtrG7X29aBGwcq1LYAcRbfZP/557zPv9+0H3jlJAXJxf5ZKsLCCpvYnNW656+7UlVBWM\nmiKc7JEtfBY2KKXiAZwG4FfXap8C4DvyQgCzazSeDRDRJgiCIAi1SSBLW1ERuay563+x6DnmGKdo\ne/11U4fLns/WW7cFz16HrRXr1tFxOYPiH39QMgLACKTVq41w5HXYmmVnJbThTmCnTnSMe+/1r4P2\n/vvUI2XrdceOZllpKR2H97N+PU3btHHW3uLkF2ylsd3lysroxZnyWrQgl8UmTcz6xcVGNHDyhzPO\noOny5c4EFlFRRmAPH27aMHeuec+d+SlTKLMgw7FynCHPTrHOTJ9OCWQYdo8sK6MO/m230XfI23qJ\nNjs+0SozAsBZo8sWbbwfd6HylBRnj53dCvlYtssgQBYTjtkCKJbt00+pnxmOaLOvCV/HyEhaPznZ\npM5nIiLo3gTIHHTjjY7mFCIRnxcPI/ffqCjT3y0vJ6Hx/vt0r8+dC8THV2Q2ZEoQZ4SeXStPKX9X\nPq/vMxCZmeRim5KC8nLS6cFKNdqibcYM4M47TRJZjQiMjv0IszGsYn3+yvjnv28fuS2yxaysDCiD\nK/lRhw7YuJEqEnzyCRn6d+2in8D06d7t2rwZJNqaNAGU8qs4kJUFZHQwceJri9P9brHly8nAXcNK\nx49w3CPTAExTSkWCRN57WuvPlVIPAViitf4UwCsA3lBKrQewF8DoGmsxoxS9RLQJgiAIQs0TSLTd\nfTeJNjcc49OjB7lR7dtHPbDLLwf69XMmjACMaCstJffAmBj6bK/DFjLu9J54InVuZ82i3lhcnLFQ\nsRsj88sv5r2XaHvmGeqI//knWSpmzKDh/yZNKC6KjwlQqvuWLel9u3YmmQKLyl69KKMCC9cWLcj6\nxe0vKiIRx0KgZUuT9MLOdJieDvzzn+a4tqWLRcPNN1Mv8v77yTJWXEzCguOXSkqo9/vPf9K1//pr\n6lHbgsS2wNgueN9+Sx17doMM1Mm3kyGlphrzygsvmN57uKKtc2f6fiIi6PwnTTK1x5pb6RLGjyfr\n3JlnOtvijtsaNoy+H/6+lAL++lfnvgCyiLmLL3uJNo5r4+sf6DomJBhrYKtWZIXmtj32GL2Skyk5\nS3x8xe+rEInOkMaLL6YgKnemTh/5MMdcjL4AlDFTpaRgNk7B1xiOCtk4dCi5DwOOPrTWzq8xGIsW\nAbfcQu//53+81+F9HThAt+jOnc6/EHdYqFu0ffUVbffTT6Z+9b9wG87Bp5iBCzD2/CIopfD005TD\n5IUX6O+lXz/aJhBZWcAxgwYB+fnQ2t9hYN06X5WF22/Hjx/9gZxc5ffX9/PPlDy1Rw8qE1hbhJM9\nciWAXh7z77PelwAYVb1NC4PISBFtgiDUGlprv0yNgpOadrIQahmtyV3s/PMDi7Y1a7y3ZcHUvTuJ\ntk2bjIWNh8+DuU3u3k2d7aws6hgnJxvRw9ux6xu7mC1fblzD3D5MdvINt2i75hpj8bjoIuotMjt2\nUCZEO+nBuHHmerRu7d9+trSxix6nYbfdMm0BlJJizBbcY2/SxP/62NuwaDjhBBKUdk/ftrSVlFDB\n5AsuMMlI4uKc+7JjnWzRVlZG583CxO0e6UVqqnH1HDzYXE92c/USbTYZGWSdOvZYEtpsrQScsWI9\ne3rX0LPTzn/7Lbm8/vvfznXeeMN/u3nz/OfZqd0DWdoiI008pH0d2ZrDvPSSeX/xxfRi9u+viOUs\nQJKj6sKnqztiwNYSeEUVLVwIrN7WDCcC+Bxn4mx8Tgt8ok1Hx2AYSKBViLZvvyUL6PPPozTXKJbd\nu42uDQULrmC5g/jvIT/fjENwaHRsrPN2jYryF228nEO3i4qAJTgeiaAVznsNmDnd6E/+S7HHVrzI\nygKy/jIO69uMQ9ZL/ssrfup3P44PyoHdzzilRufO5lZ+9dXaFW21l/KkJhDRJghCLREXF4c9e/aI\nKAmC1hp79uxBnDuRhNBwWbGCEmCMGRNYtAVyNWQ4rmbrVtPLYxe1rCynRYIHRfbto5iiM84g4ZaR\nQdYqW7SlpBgLECc6SU0l0cZWOoBSrMfHO4f23W12x9FxxzwhwVkf69RTSeyMHm2SaXgVI3bHmXXo\n4B9PZAsgOwaMe7j2OXht47YUxcaa62Gnire/NxY0cXFOQWELKXfPvVs3s2447nSpqaZe2pQpdO8A\nxlJkH4sFNkDiEzC1xLgXHqCWVkBs0eauG3Yo8D0ycCBZc203Tr4u9vHcoi0YMTEVFslCJFbcAlu2\nUAWFq67y3+TAAVr20290X5XbXXpfrcOi3oO8jzduHABgX3/jnhgsV5Ab/hkHE2389eXkmL8KNki7\nk14ec4x/CUO+bdl90e01/csv5NlqV7LwWs/N9u00fjF8eOCSkJxgNDnZv2zh8ceb959+WrsukmFl\nj6y3iGgTBKGWaNu2LbZt24Y/vQqxChXExcWhrTtmRGi4cM/r449NDBcTrmjjuJrsbCOc2H0rK4sE\nWXEx9RqPOooscvv2mdT5//0vJYYoLqZ9LF0KzJ9P27FlY+NGEnwpKdQ36NGD1rvgAqqL1rWrMxW/\nu83ugQYWbSww4uL8e2fJyd49tvh4E48GkDUoNtYzCUQFKSnUOywpMSYGt5B0b+MuNAxQxs5nn6V2\nsYCwTRos+tyizfYgcHsTjB1r/M3CsbQ1b05izL429nu+plFRzmPNmEGxfxxrx6YXq5179lCpvUEB\ntAgAp3uk21UyBN9+S/vmSz93LnAyL+SZI0f617nztVGnZ+Drr0jPR1ZCtOXkKiTGJyG6IAcFSML+\n/eRdy8ZXr8QXn39Ot3EBVUJzirZTTwW0Rt4O/+0AkEDWGnlbzKysLLIarV5Nt9lRR/lvtmsXCUn+\nGa9eTUIsMtKZ82T5cmMh27PHaRCNivJPOtqpExnsv/jCfO0s1nJyyNjqFoi2WOva1dQWd7Nnj3N8\nwy3qNmxwhqUC5qfqpfntXEW7d5Pbp5exvSYQ0SYIghAG0dHRyAhSE0YQGiV2Qge3G2S4oo17aHv2\nGL+i7GyKe1q8mHqKLKhs0ZaUZERjly5kqdu8mSxnAAky7l1t2kQ9M3ZnO+44Em12Ed9ff6X18/NN\nIhAmkGgL12rMaeABEqm83RVXmM6+W7TZNb9YXOTkBBdtdk/arjfGjB9Pou2UU7wFC1vRbr01sKBg\nfy/uCf/lL8bnLJil7c47KW4ulFBiq+qdd9L0+ONJxLdpQ5/52nuItiuuAD77jL5uL1EBoMqWtpUr\nSeuMH08hgBs20DhFhdz0+j4Yn3nol8IMnH468PjjwO3HHOP/nQdg9GjgpYJEtEcO9oHOd8gQUy7P\nLSoASroBmOQcfkk64F9fO0CzARiRxTlnvMYjRo0iMRkZSeJl/Xq6PaKiaNyEf352UkcObWVSUvy/\nlu7dKZHIiBFmHtdm37vXO/mpHaJ64YXGE9mN+3bct49+OsuXU6Ftd2JXwNzm7m0TEsyy444j4bhs\nWe2JNnGPFARBEATBG7vXx6KEWb+ehuft57BXzGdqKiWVyM42poPt2ym9/I4d5D7HmRW5Jz5njtMv\n6dJLSRjZMWEREc5OsW154o4/9yK5Z8aZCEO5R7LrW7ii7c8/TbYEFooHD5qeNeBfI2u0lbONe7E5\nOSa2yss98qij6Brm53uLo2OPpet29tnegqVpU8pCeOut3vsHqAddVkZC7eBBZxbFYKJt8mTad6hi\nw5GRtN6kSfR54UJKxsGwmOX7yoor4456kJKS5rooFbZoAoxIYAuOO6tgUNHmGwRYV0rf/datoDjO\nlzyCpjz4/XeymB2MjkeZz55SVubMz2KzfTvw5Zc0PhCBcgBOS9vcuRTrZf98FywgS5lVztOz7KHN\n3r0ml85PP5n3ZWWmrN6OHWR943DJ8nL/ttokJ5tbs00b2ped5NI+Nh/Li5Ur6dYoKjLVN7ywb8ek\nJBJthYXkhWsnDbVhg7L9E3r/fbpePFY1eDDdYsFcRKsbEW2CIAiCIHhj9/q4FxUbS8/f9ev9h6nZ\nNfbkk828iAgjuGzfpyefJHF01llGtHHv7Y47jMUpMZESjqSmOnvSQ4ZQW1h82KLtdF85WS5AxUKq\nRw/qabl9pNzijK1YV17pviLeKGVEGV8Tt/ufW0DYiSi4d3jvveaYgURCs2bB3RT5WgayeHH27WBE\nRNA6nPkxI4Pa45U4pDL79Vo3IsKZ8IPP7dJLHZvoM86o+Po/+yzIvvlaNmsWWkBasNjgqZ+lKZho\n8/FHAt1nFflSwrwebdpQLFtxpFMUswHaFlcACbbycuBvfwNWoxsA4DOYIt1DhlDSzIULzTYDB9LX\nZzuMsKWtbVtv8XHyyRRvtnw5eVRyTbWhQ/1dVHl8wu3KyX8bTEqKuTW19h97YfxEs4tffiEjfny8\nt6ewF23amCS2ycnO287Gy9LWpAm1lQ3R559PFkB3FsyaRESbIAiCIAjeuEVbcjIN159/vvf67OLm\nHjpPTfUF4BQ43fJWryaRwULDnSLvvfdMJkK7Z/bww8D119N7Fkv28kGDyDLI6eC5p2onBLHb4RZt\nLVrQ+XLtt3DgdgRyo+bjnn029Ujtnir3Dj/+2MwLQyQEpbIJPIJx+un0PXilMKxulKL7zi62XVCA\nrKmfVAgCt9HXAV/LSiYhYQHjFm8V+L6PAwdMElQ3O6LJUlyZXEybN5OYKkQiCrS3JdOdlp5/lgMH\nAmvQDc0jcvAG/EsCcMFqNxs30vmxGDzpJLIOsihjOHHIe++ZeTfdRKLZ/rlFRdGtm50d+LvhsRXb\n0sZdeC/R5hZ7bvLzzU+N2+LlMWyTnEzX0q717kWgvDIA5Ufau5c8kD/4AHj55eDHrE5EtAmCIAiC\n4I0t2nJyaFg7Odm/d8ewpS0lxRn9n5pKvVOtjSWqc2fT2+JgFnfwSs+eRnzYvcQOHYwVhY/jHm63\nMxN2I2sEevQwPcSEBCMWvQRSSkr4liPACNYePbyX83GbNvW3gnkJjEDui+HCbbfT3XnB7Q61r0om\n9TgkkpKMlQ8AEhOx6jf6rk44IYQVJj7eFLauBHyrc7fSr3vpu0cmTyYvVUe2xT59AAB/5JFaC5XB\nkNmyhcY3PvgA2Ip22FzqncTJLdq42DP/XIae732u7syKTMeOZNDl/XI2R7u+mX3+tpdv9+7007F/\n3pdcQmKWhZsXbKS1LW3BRFsoSxtgxoa4LS1bUoUJN/zzb9LEWNpsT99TTnGuz4LOTqRqj1fwz7VH\nD/8krjWJiDZBEARBEPwpKnKKMxZtgEko4oZFW3Iy9Wo5diw11dRS46h9O4XcXXfR+lx3DaBAHzsL\noy3KbJFz7rk0defmtuncmY4/fLixiMXFGZNIdZSpOPpoOgaXAnDDx/WygHkJjEO1tAFkHePiWF7s\n3h047V49g8Mhjz6aBJafJczGle2iqMi7pJsNizber9/t5LtHuHTe669Tm8rLQdd41y7s3OncVyjs\neK//wVM4p+xDv3WaNPF3jywupi5wXBztw6vsHOBM1pGYSIlcmGnTTIwa/xS3bTPL7bBPO28PW7di\nY424OeUUur2XLg1PtPFXw9e6qqKNxxvi4+k6paYCb7/tX75g0SL6KbBoKyx0iraZM4EPrUvPy1JT\nyRlg2TIKF61rRLQJgiAIguBPaqrT96egwIg2zuAIkBWGh7wzMshC0qYN9cR4eDo11fS62dJmp4qL\niHAWcQb8rW62aLNFzuWX09TOxuhFhw7O5BSxsUYYVYdAso/hhZdbJuMl2g7V0gaQqcAuSO2mRYvw\n0vjXA+z4K6290+BX0Lq1I6VfkyYUmxUMt2jzE3m+e4Rv6XffJfFx002gXn7LlpUWbaWl5n0BmmIP\n/IOzevb0trTx19q6dXhjDmPHOsdEAOChh2jKPx1uPxA4wYadtZN/ki1aUGnCZctCi7bExOpxj7T3\nCZBrZLt2JA1YWPLfVZMm9FOwRZt928fHOz262QAPkFhzl12sK0S0CYIgCMLhhNbhVYTl6rZeRZjf\necdkxjvySNPratOGhtvHjnXuyxZcZ59NQ9+33OJ/zGACI5ClrUsX8gN74IGgp1MB+zNVt6UtFMFE\nm91LZKpLSDYwAt2eLFw4bonHADyZMQP4v/9zzNq6NXiXkUUg3/acB6eUU+n7vg9ux+bNNH3uObMP\nFj15ed7HKi+nc2NhaIu2QHTtaixtvK0t2gLBYyMJCfSTfOop59gA1zIHjBDdYdV143i4l16iy7ln\nDyX59DJ+p6ZSmv+VK00Iqhu+/SMjTTv4OrgTqwL+QtULe5xm5kzg0UfN56ws8x0xTZqQGCwt9U+E\nWht/AYeKiDZBEARBOJx47z3q7QRzJ7R7zXYwB4u2pCQTCJKWZoRIkyZkGnALDltwNW0K9OvnbZHi\nODWvjAJ28IjbMtWzZ+herLst9Um0eXGYirb0dO+wwIICumXYgBbUfS493ZRtsIiKoqLUXrB1jMUg\nW9rWNPWlC/R9HxyvxuKOKS83LoUzZ/pbtQDqtqal0fS334L/BJmUFCNg2ren+LNwRBvXW4uOJkEV\nFeUc65g61bxnAWOLtqVLaXrqqZR36IgjKkL3KrBFW69edM1+/NEs79fPvOe/jogI8/PlcMtAlSS8\nxjJsbEtbhw7Ov4j0dP+8OU2amPvGbWBuCD83EW2CIAiCcDjx66/UuwxmqrB7xHbwkN274h6TW7R5\nYceveQ2r2yxaZHqMNhy4AhxaUgzu2dWEe2QwQom25ct9Bb58VId7ZANkyxZTy9uG45A4v0w4MU9e\nvPii93y7GgVgRNvdPT4jJeL7PrySjOTn03Z2l3TVKqfViffHYaI//ui9r+ho4IsvyEq0ciWJi6Ii\n2ve2bZTGPxzRduGFNLXdSO2fzXHHmfd8S9rukWvW0NROxuHGFm3sNrl8OY0JzZkDTJ9u1rVFW0IC\nFenmOLLISLoe7oLpXmMpX31l3gerQOGF/dMTS1ttI6JNEARBECoHD9sH8z+ye292z9Ienk5IoKH7\nNm1CizY7DX6ogsf9+gVOLd+8OR33UAQN9zTLy2vX0sZqI5Bozcw0iVyAhjH0X4sUFNDtx9aiU08F\nZs3yXvfYY00WQXZzZNxib+JECp9k0bZ/Pwks3q44NhkYMACFhWTpmzHD/3gnnWRuK9uAbMeFud0G\nS0q8RVtEBFVYaN+eLI4sLux1d+zwF23u2+qMM/z37ZXCHqBbLSLC+bPfsIF+ziy2vGjZkn6KzZoZ\nAbVtG12LIUOctzO7a3LykBNPdLZ5wAD/2m+xsf5/BcOHm/ehwljd2H9PbkubiLaaRkSbIAiCIFQO\n7v25U9LZBBJt7uHpGTOAu++uXtEWjNTUStff8twHQOdfm6ItI4NcUy+4IPh6nOr+MLS0BUuVz5Y2\n21r0xRfe665dC7z/Pr133+Z2jbWyMuDxx6lOvG2RyskxljH22P3998Bts1Pr2/Xm7cLL9k+Kz8fr\nfN0ik8WF3b4FC/xF2y+/UE16JiGBjm+n8Xf/dNauBebPJ6HZpIl/JY9Q6ewnTKCabUr5JwUBnO6N\nN9xAWS6vvTbw/p5/nn4i7B4bF0fnEMiltbLjGsEsbQ1hjEREmyAIgiAcTgSytF10EfCPf9B72yxw\n5JGmR+Menj7lFGfWx0Cize4tHkq2whYtqk+0FRbWrnskAIwaFTqmrWtXmoYK6GmEuIWNjdvSBjhF\nUSDcmRy3bDGZCefMMfPd1S3cos1Ohx8MWwwsWwbMnUviZ+BA53q7doWXbIP3Z3sYA/6irV07p3Ut\nLo7izOwyfW6v4i5dTLvsdnfsSFN36UM3bdoYy1eTJuan7VVfPjIS+OtfaRqIxET6ifC5xcWRte7M\nM+lzZcomeiGirS4R0SYIgiAIlcNLtK1bR6aJ++6jz9x7fvFFYPZs06MJlDEglGize1vBem2heOgh\n4Iknqr494BRttWlpC5cvvwT+85/aLWZdT7BFm7t7x5Y2+xZcsSJ0N9Ar/T7XHbOF2Pbtxl1v3z5j\n8eJbl+uzhQtbiaZO9U5fv3On09IWSJAEGuPwimmzjbMRHj38YHFw/NNNTDQujKFEmxt2pbStjVWB\nb31bSM2YYUoKLlgAfPtt5fcbzD3SruVeXxHRJgiCIAiHE17ukdOmOdfZuZN6ONdcQ8PunE0ykGjj\n+eFmRqwq/foBw4Yd2j7qyj0yXFq3BsaNq+tW1DrLllFsGNO3rzMfDYs2W9wUFQV3WwS8RRuPV9iu\niAUFJkbKLsb91VdU0HvCBLNuMPHDiTuGDQM2bSLXvv79/ddzi7ZAAimQmAsl2iqzL8D8dJOSjKtj\nZUUbXzMvS1tlsBO8Muefb8oN9O8PDB1a+f3af1+B/srqMyLaBEEQBOFwwsvSZvuZFRaSKcJOmc4F\npQIN+//1r1QIKlivcf58CmqpazhQZ//+2nePFAIyZYrz84oVlGyEYfdIAHjtNeDJJ+n9unXO7exq\nFUVFJhZs7FjgmWfMvgD/+DEuJ1BU5Fy2YYNzPTs5httC89prwKRJwM030+eyMuDf/4YfbtEWKPfO\nsGHAOef4z6+KaAOA118nS5UbFm0tWlSvaPvuO4pTqwx83Or+WdpjSl5VRV5+me67+koDMAYGQUSb\nIAiCIFQOFmsFBRS8c801zl7cpk1Adrazx8bP2kDD0507O6vuejFwoH9gT11gB0XVR0tbI2HxYirM\n/HZWi+MAACAASURBVMIL4cUieeXFyc2lV3KysbQBwOWX07jCLbeQ22JZGRknb7qJ4riY2bON4Lnn\nHmN187K0AUa0Pfts4OQXAAkbLtzcvj2wcaNZlpZGx8rOps+DBpHV0M1vv9GLCSSQ4uOBV181y1u2\npPg7L9EWjshx171nWNCkpTmrYlSG+HiqPWe7R558cuX2AZhz9XLxPBRYYPfp4x0yetVV1Xu86kYs\nbYIgCIJwOGFb2j78kIJFcnNNvu2sLH/RdvAgTQ8liUh9ITISeOQRKngllrYaY9YsEm121sNgLFtG\n1g93J3/RIpraljaAbs8mTUgw7dgBvPkmuc2xtQdwJups1sxsH8jSxrFcbsHWogVZAlnv21Yxd4IQ\nu31//zvw6KP0+c03gdNOo/dc+Np9jEBwtQiAXDWBqlvaAmGLNt4P/+zD5dtvKSw2VCnGUPBfT7Bs\nolXh+OOB664DPv20evdbW4hoEwRBEITDCTumze5dcZDIxo3+oi1UTFtD4667gBNOIAtbdHT1D+kf\nBvz0E7kBuikvB267zXjcesWUudm1i9wFb7sNmDzZuWz5ctrnvn3O208pcsN76ing3nvNfNuV8cAB\n875pU7N9KEubm4QESqc/ciR9tgWWl5sd89BDZixkzBhg/Hh6f8YZdAvaBHNFtC2VXIC6ukUb7y8t\nzYxhlJdXbh99+gAPPlj1NjB22Gl1Eh9PZQUCfc/1HXGPFARBEIRDQCl1OoAnAUQCeFlrPdm1vD2A\naQCSfev8TWsdoCxwLWBb2uy4tn79yJSwZo2/aGMai2hjzjmnYaSNq4eccAJNbcEEAKtWUcZEJhzR\nxlkcO3Z0lvHLyCDxV1xM4wbu2699ezre66+beXZdMpv4eLM9iwHbKgcYS5sbFjTsUseWr7g4I+BG\njwZGjPDenmFLXUqK/88rXCM2h5p6ufcdimjja9GqFbmfzp/vLyxrCzvBq2Bo2P9UItoEQRCEOkQp\nFQngGQCnAdgGYLFS6lOt9RprtXsBvKe1fk4pdSyAWQDSa72xgDFZANRztXvURx1FAUELFlBvyUu0\nNQb3SJtTTqGXUGVKS5261+0OmZ9PBawvuMA/q+D8+ZTGnV3+EhOdoq13bxJtLLLct9/u3f7t2brV\nu51KGdH25pvAMcdUztIGGKHElqiEBPMzeeSR0FkTOZV9crL/uuEKLnZjLC72X3YoXr78V5CWRgb4\nyiYPqU5EtHnTsP0BRLQJgiAIdUs/AOu11hu11gcATAcw0rWOBsBd0WYAdtRi+5zYPb3CQtPDPu00\n6sX27g388gvN42wENo3N0iYcMm5rlV2kGiB3xYkTgb/8xX/bm2+mpB3cOU9MdHrs9uhB3rpsiXPf\nkm5XSsC/CHbr1sCNN9L7qCiyji1fDpx9Nok2uxxeixbeZQRZtLGwYpGakEAZLs8+m4pAh+Loo+k6\nnHSSt2i77DJy3/PihRcoZxC3pajIf51DsbSxaOOyB3WJiDZvRLQJgiAIQtVpA8Ae29/mm2fzAIC/\nKqW2gaxsN3vtSCl1rVJqiVJqyZ9c/be6sXtBhYXUU4uPB77+mnp8vXub5YeDpU0ISXk5pdcP1IF2\nW3x27XJ+zsmhqfuWXraMxNOBA07RZqdlz8ggt8jvvjOfbYYNA374wXyOjDSijROC3n67SfUPOG/h\nvDynZS8lxRSItgkm2o4/nhJbeLkreu3nyy+Bbt38E5jExFC5xOuu89722mup1n1NizY7uWpd4TVe\nJIhoEwRBEISa5hIAr2mt2wIYAeANpZTf81dr/aLWuq/Wum+LYKnkDgW7583ukXavdcAA8/5wiGkT\nQvLpp1RY+p57vJe7LW07dzo/swuj2xXxlVfMfPbYbdKEcsIcdxzVNmORNns2Tb3cD+15qanA9u30\n/oknqArF2Wc717erOyxZ4nQpTE72DnFkoXTllSTqxo6ldgYrsh2K6GgTFwiE79p4wQV0na6+2n+Z\nl5UwXB57jKxsHTtWfR/VRXQ00KULlV4QDBLTJgiCIAhVZzsAO39cW988m6sAnA4AWusFSqk4AKkA\nPCJyahjuHStlLG22aEtPN+/tPOOMpMZvFGRnAz/+6F+0eeFCskS1bUvG11GjjKUsUOr+cEWbncmx\nuBh46y0z37a0AabAMQuw2bNpmZcFxnbna97c1E/r2RP49Vf/9W0L1YYNzvT7UVHe3UoWZ507m+0T\nEg5NtAF0vW+/HfjXv8K3krVrF9jqGU49vECcc453Ee+6Yu3aum5B/UMsbYIgCIJQdRYD6KSUylBK\nxQAYDcBdBWgLgGEAoJTqCiAOQA35P4aAe3tt2lBxK7d/GECp+Jo0cQq4u++mXuWh9AqFesNZZ1H6\nendmxwEDSMTcdBNw0UXAzz+bWl2B3P/c7pFu0cZukXZ3belSuvX69KH53A63IZfTz+/fTxY1r9sv\nIoLCMW+4gSxtPC4RqF66W/C4xyG80tx7ibPqEG328Q/FtdEmJqbusj4KNYuINkEQBEGoIlrrUgA3\nAfgKwFpQlsjVSqmHlFI8bn07gGuUUj8DeAfAOK258Fktwz3WQYOoN/3bb/6VcMeOpfXs4J+HH/b3\nbxMaLGzFCNSF4mQiO3aEFm0lJbTOzJkUf+aOafPK8JidTdMOHWiak0P7dwuXiAhTlyxYZsbffiNX\nOtuj1ys2DfC/jcMRbV4CsL6Ktv37KZOl0PgQ90hBEARBOAR8NddmuebdZ71fA+DE2m6XJyzaBg8G\npk+n1Hw9e9Ztm4Rah7tOLMjccDKK3Fxv0WZ3vUpKgDfeAK66CpgyBfj9d+e+gok2TrG/d68zAYlN\n3760z759A58PY4u2QJY2JiaGXDNjY4GuXY3bo1e30kuwdu5MFr5DpbpFm9B4CWlpU0q1U0rNUUqt\nUUqtVkrd4rHOEKVUnlJqhe91n9e+qh0RbYIgCIIQPuw7duKJxtfM7R4pNHrCFW179hj3R1u42C6G\nxcUm3m3iRBI//fub5bZoW76cpl6iLVCOm9dfJ8ufu4i3F5URbVwbLjaW6slv2kSf2dI2b56xWHkl\nJ/nySxKphwqLNgkXFUIRjntkKYDbtdbHAugPYLyvOKibeVrrTN/roWptZSBEtAmCIAhC+HBvu1Ur\n4Fjfo1xE22FHaSlNA4k2tnrt2mXizWyHXjsWrqTEmc7/uOOo7J/Xun36kCtjdja5L3KNtGCiLTIS\nOPLI8MIp7aSrgUTbuefSlPPsuMUSdyuTksx18hJt1YVY2oRwCSnatNY7tdbLfO8LQD777ho0tU5O\nDjBnXiTKS0W0CYIgCEJYsGhr0gS45BJ6z2YP4bCBrUmBRBvP37nTiC62uG3f7iygXVJC4q5VK7JY\nff+9f5gkozWl+s/OJqsYC5Vg7pGVgePfgMAxbe+/Tz8DFolu0cbiVESbUN+oVCISpVQ6gF4AFnks\nHqCU+lkp9YVSqls1tC0oy5YBv62LRN7eMomNFgRBEIRwYPfIhARKOAJUT2CO0KBg0Wan4bcTcLBA\n8xJtbdsCAwc61925kxKSdu1Khls23nrVDZs2jdZPTTWCJZilrTLYyUoCiaCoKBKInGcnkFtiYqKx\nuoVTOLuqiGgTwiVs0aaUSgQwA8AErbUrSSyWAThKa30cgH8D+DjAPq5VSi1RSi35889Dy3Y8bBhw\n2unkHjlt2iHtShAEQRAODwoLTQXj9u2BLVsCV00WGj22pc0eAOfabG7Rxuvb25WU0HppaWYeW9pa\ntvQ/5u7dVHfNtrTl5FS/aAvlThnI0mYvr01Lm8S0CaEIS7QppaJBgu0trfWH7uVa63ytdaHv/SwA\n0UqpVI/1XtRa99Va921hOx5XkQ6dIhEdUYbnnjvkXQmCIAhC48f2CwOoUq8M8R+2uMUXwx6zdkxb\ncTFlk3TjJdr4PSf7YM45h5aVljpF24ED1eMeGcgt0wu2tLlj34YOpWl8fO2INk6ewslfBCEQ4WSP\nVABeAbBWa/2vAOu08q0HpVQ/3373VGdDPY8bRaLNXRNEEARBEAQX5eVAQUH19I6FRkEgSxs7QxUU\nmMyQxcXGAmdTUEDr26Jt0CBgyRKa2hx5JDBuHL23RRtQPZa2yhDI0vbJJ8Cvv5KlrjZE25AhVGy8\na9eaO4bQOAjH0nYigLEAhlop/Ucopa5XSl3vW+dCAKt8hUOfAjC6VgqHRkYiQpehoKDGjyQIgiAI\nDZvISODtt2u/dyzUW+yYNi9L2759xrrmtrS1a0fTrCxK3mGLNqUoU6Rdnx2geVdeSe/T0upWtHHb\n3L3VxESqwQYYd0s7wUl1oxTQu3fN7V9oPIQcO9Ba/wAgqGew1vppAE9XV6PCxifa9u2jAcSISqVV\nEQRBEITDEBFtgo9A7pF22oGdO2nqtrSlpNB6P/1En7t399+/fat9+SUwfDiJlHnzaP01a8zy6jIA\nb9tGiU1CwdklbeHq5uabqTLGqadWT9sE4VBo2DLHJ9oAZ6FHQRAEQRAs7NSA4h4p+AjkHmlbn1jM\nuS1t/fpRPNiaNSTEjjvOf/+2aPvLX0xykEGDqEZbTVja2rQBevQIvR4fO1DZA4CMAaedFl6NOEGo\naRq8aFNaA9DiIikIgiAIgbArHIulTfARyNLmhW1p+9e/gCefJNGmNdCpk78rJOA9z6Yu3SM5jX8w\nS5sg1CdqMLSyFvAVAIlEGQoKGvapCIIgCEKNYZtI3OnyhMOG/v1NdkTAKVjcNW8TE51eTLZou/56\nci/kWylQTFYoIVaXoi0cS5sg1CcattKxRFthYcM+FUEQBEGoMexgpKKiumuHUKcsWkQvJpilrUUL\nI9patjTukbGxJh6Mp4FEW2UsbbXttSuWNqGh0eDdIwG2tNVxWwRBEAShvmKLNnlgCj6CibZUq9pu\n69ak9XNynPXE2NLWq5f3/kNZz+x0+3WV8p+FpyDUdxqNaBs6FPjiizpujyAIgiDUR2z3SDu+TTis\nCZSIBCBLG5OWRuvu2UMJRJhDFW116R45YgTw0EMUnycIDYGGLdp81Q4jQRkkWbStWgV8+imweXNd\nNUwQBEEQ6hFeVZGFwwqv6rmB6rQBTtHWujVNd+zwt7QddRTQvLn3Meuze2RkJPD3vwNHHFG7xxWE\nqtIoRFsUqGT9+vVkvh81Chg5kooizphBq+7f7/zDKiwEzjoLWL26thstCIIgCLUMW9puvJEKbAuH\nHV7ZIb3cI9u0oanbPRIAVq50FpoeP56sVYGoz5Y2QWhoNOzsHS7R9sUXZqTm6quBxYuB224DBgwA\njj+ezPtffEGjR598AsycSYGoJ59M3iJ33AEkJNTVyQiCIAhCDZGTQ0Wnnn5aik4dpoQSbewemZYG\nbN/u7x4J0MC4nXTkgguCHzNUolIRbYIQPo1KtNmcey5w5ZXAkCFm1GjHDuC884BTTgGys2leXh5w\nzz30R3T//cDEicA//kG7/uknYN06YO5cEn9t2wK7dpE/9549wLHHOo9ZWEh/OqWlwO7d9AfYtq3J\nUCQIgiAIdUJuLj28RLAdtoRraWvViqa2a2P37uZ9oEyRXoS63SIj6VVWJjXfBSEUjVa09e5NI0Pv\nvUcCDgAGDQJ++AGYP9+sN2eOc7spU4B33qFtFy8281991f/wY8eSmOvRg1wyP/oIOOkkYMsWYNMm\nWue664AHHjB/goIgCIJQ67jT/gmHHcXF/vMOHADuvZcGmps1o3ncXykvN+udeKJ5HyjpSFWJiaG2\niWgThOA07Jg2X/ZIL9HGpvyRI4H33we+/JICTlu2JKF12mnmj2fgQODbb4E1a4DJk4Ft20iwTZ5M\n7pTr1wOvvAI88ojzGG+8AVx8MVnoPvoIGD6cRGHr1sBTTwGZmcALL1Bbxo831jxOkKI18OabwJ9/\n1tQFEgRBEASQm8iRR9Z1K4Q6xMvStmUL8PDDwEsvAbNmkQctx7LZCUeVolDIyy6rfOKOa68Fnnkm\n8PKYGPMSBCEwjcbSlpREfzg9e/pnM77wQvN+1y7z/rPPgHPOoSDaoUNpXteu5Dq5cSNw553GtN+x\nI01jY4G77wZefpmyVD72GP3hXXYZ8NprVP6maVNa96KLyN/7iCOAZ58Fpk0D9u0jt8unnwa++Qa4\n4QYSftOnV//lEQRBEAQA9FAbOLCuWyHUEZs3Ay++6D//99/N+7VrqWYZG2TdCUcvuYReleWFF4Iv\nj4khsSgIQnAahWibN7sUUZnmj4ZFUyjOOosEmjtV7ZQpgbeZMAEYM4YsdgDQty+5WP7znyTw7GO3\nbEmWt/JySnayaBGJvi+/BI4+2qz37rvArbcC/foB//43Ccn09PDOQRAEQRCCUloKbN1KKZWFwxIO\n3XDz22/mvdaUOGT0aBqMvuIKYOfOmveqjYkJnbBEEISG7h7pE20tjiir0p+KUoFriwQiIsIINoCs\neM88E9wXOyKC3C9zc8kVkq12AI1atWwJ3H47ibpbbgGuuaZybRIEQRCEgGzdSpkeRLQ1Or74gkI6\n3GzeDEyaBMybR5+9BFtSkgnP6NqVprGxQPv2lKSte3dyiQzm2lgdxMZK5khBCIdGYWlDqX9MW33D\n9tdetow8VY45hua9+SaNaA0YQMuzs+mUysroz0wQBEEQqkxWFk1FtDUqtAZGjKA4+u3bncuee47C\nN/r1owFhLxISKKSjdWsSbWvX1k3StJgYEW2CEA6NwtLWEESbTdOmlKQkIYFOYdw4SlDCbNgAnHoq\nZaUUBEEQhENCRFujhNP179jhv4yTiBQUeG8bFWXKEWVkmMySl15avW0Mh5gYyRwpCOEgoq2e8MAD\n5L7w5JP0Jzt3LtWIk8ySgiAIwiHBZpi2beu2HUK1UlQUeBknZPNK8w9QHD6LtvbtTYKRMWOqr33h\ncswx/nVvBUHwR0RbPaJdO8o4OWgQvQeAGTMo+ckff9Rt2wRBEIQGSlER+dpzL11o0CxaRK6R4Yg2\nr3U2b6bkZxyyceSRVHe2tNSUS6pN3n+/5uPmBKExIDFt9YxWrShwuKiI3AVuuIHmHzwof2qCIAhC\nFSgqIn98ocEzezYwbBgwdSplwA5EXh5Ni4pI4NnEx9OU53NdNl/pW0EQ6iliaaunJCQAZ55JwcGZ\nmVTc+4sv6rpVgiAIQoNDRFujgT1dlywJ39JmF8kGjGjjdVi0CYJQvxHRVo/5/HNgzRoSa506UcKS\nV16huir79wN799Z1CwVBEIR6j4i2RgMLruLi8ERbebl/ohKuicbWOBFtgtAwENHWAGjVigTb7t3A\n1VcDXbpQ4cteveq6ZYIgCEK9R0Rbo4HLAIUr2gAq02fDXSdOUiKiTRAaBiLaGgjHHOP8/PnnlG2S\nR8oEQRAEwRMRbY0GTvPvFm123JrW1DdgMbZpU/B9Nm9erU0UBKGGENHWQOjUyfl5+XKabt5c+20R\nBEEQGhAi2hoNJSU0dYs2O7V/cTFQVmYKZa9ZE3yfYmkThIaBiLYGQocOQEQEuUbYGZ5uvBFYsKDu\n2iUIgiDUc0S0NRps0WYLNdsdkt+zaFu9OvjXL5Y2QWgYiGhrIMTEAOnpQI8epoYbAMyfD1x5JXDp\npcC+fXXWPEEQBKG+IqKt0bB/P03dlrZQoi0jI/A+uV6bIAj1GxFtDYiHHgLuvdf/2fvrr8A771B9\nN0EQBEFwIKKt0RDIPfKGGwCl6D3HurNo++OP4KJNEISGgYi2BsSYMcDIkSblr5uVK2n6yy/Arl21\n1y5BEAShHiOirdEQSLTNnk3T0lJ/SxtAom3tWmDxYjPv99+BhQtrtr2CIFQfUXXdgEPiMBNtzOmn\nA0uXAqNHk6tkcTHw9NOUnGTbNqBnT1pv3Trg6KPrtq2CIAhCHSOirUFw4ABw8snAI48A33wDfP89\n8MMPZvn48cCLL9L7oiLvlP9793qLtg4dqFyQzf+zd97hUVTrH/8eQhIgIYUAIfRepSMqNq5dVNSf\nioAK6LVi49qwXBVQr3JR8aqAFzsWFBuIYr9SFaVIVzAQSiI1BBIgISSZ3x9vXs6Z2dnNbrLZZDfv\n53nyzOzMZObs7JTzPW9zJjgTBKF6I6ItDBk/Hrj6aopvYzIzScj98YdetnChiDZBEIQaTUkJjeyJ\naKv2HDhAlq+VK4Gnn6ZllqXdHr//Xnd38vNpeyf79unlaWl6ubhHCkL4U6Z7pFKqhVLqR6XUBqXU\neqXU3S7bKKXUi0qpdKXUGqVUn8pprgNOo1jDRFtUlF2wAcC555Jl7eGH9TLTDUIQBEGogbA/nYi2\nak9hoX0KkAj78UegXz8gO9u+/SuveO5j3z4gJ4fmmzXTy0W0CUL4409MWxGAey3L6grgZAC3K6W6\nOra5EECH0r+bAUwLaiu9UUMtbW7ceitw6qkk1KKjgYEDRbQJgiDUeNiHTkRbtYczQ5qiLSMDuPFG\n8qQxRVtyMk1rO/yl2NKmlGdMmyAI4U2Zos2yrJ2WZa0snc8D8DuAZo7NLgUwwyKWAkhSSqWhshHR\ndpxatYArr6T56GgScKtWAc8+C5x1FrBgQdW2TxAEQagCRLSFDaaljV0iMzLsIg4AWrYE3nuP5p3d\nH7a0JSYCcXG0LCUFqF+/8totCEJoCCimTSnVGkBvAL84VjUDsMP4nFm6bKfj/28GWeLQsmXLwFrq\nBou24uKK7ysCGDiQpkeOAP/4B5UBuP9+WrZ/PyUq4ReBIAiCUAMQ0RY2sDg7dgxo1AjYs8ddtMXG\nAn2MIJSoKN0Nys4mS1tyMnWRYmLEyiYIkYLfKf+VUvEAPgEwxrKs3LK2d8OyrOmWZfWzLKtfo0aN\nyrMLO7VqkQoRSxsAnTXykktoZG3ZMuCLLygT1erV5F4hCIIgBA+l1AVKqY2lMd0PetlmiBEX/n5I\nG8iizVutGKHa4BbTtmULiTiTOnWA1FT92dTjbGlLStLr2ratnPYKghBa/BJtSqlokGB7z7KsT102\nyQLQwvjcvHRZ5VO7toi2UmrVopG5Dz+kzw0aABddBAwaRJ+3bau6tgmCIEQaSqkoAFNAcd1dAQxz\nxnwrpToAeAjAqZZldQMwJqSNFEtb2GCKtsOHaT4z09PSxjnYXnwRePNNT9HGljYAuPtuYNSoSm22\nIAghwp/skQrA6wB+tyzreS+bfQ5gRGkWyZMBHLQsa6eXbYOLiDYbjRp5Dqg2bUrTrNDIaEEQhJpC\nfwDplmVtsSyrEMAHoBhvk5sATLEsKwcALMvaE9IWimgLGzgRSUGBFm07d3qKNu7y3HknCTLzp50x\nA1iyRFvaxo0DLrywMlstCEKo8Cem7VQA1wFYq5RaVbrsYQAtAcCyrFcAzAMwCEA6gCMArg9+U70g\noq1MUlIoOclff9Fny6J6bl26VG27BEEQwhy3eO6THNt0BACl1BIAUQDGWZb1dWiaB60EYmNDdkih\nfLA4M+uv7drl6R7p/Oymx9nSJghC5FCmaLMsazEAn+krLMuyANwerEYFhIi2MqlVi6xtWVnA888D\nublUoPuZZ4CxY6u6dYIgCBFNbVA5nIGg0IGFSqnulmV5lEYOerIuQL8fnbnhhWoHi7b9+2nasCGJ\nNidO0cbeNWPHAhs3ArNni2gThEjE70Qk1RYRbX7RtCnw7rvAvfeSYAOABx+UUycIglAB/InnzgTw\nuWVZxyzLygCwCSTiPAh6si5ApxUU0VbtYdHGxbE7uF4l3i1tdesCXUsjKuXnFoTIQ0RbDSHNS9W8\nHTvclwuCIAhlsgxAB6VUG6VUDIChoBhvk9kgKxuUUg1B7pJbQtZCfj9y9gqhWvHhh8DBgzTPnqxs\naWvf3v1/nF0eU7Rxpsjt24PbTkEQqh4RbTUEb+KMl2/cCIweLadSEATBXyzLKgJwB4BvAPwOYJZl\nWeuVUhOUUoNLN/sGQLZSagOAHwHcb1lWdsgaKZa2asvGjcDQocD1pVkAgmFp42zRN9wQ3LYKglD1\niGirIVxzDU1vusm+nEXbyJHAtGnAmjWhbZcgCEI4Y1nWPMuyOlqW1c6yrKdKlz1mWdbnpfOWZVn3\nWJbV1bKs7pZlfRDSBoqlrdqSn0/TzZtpyqKNM0eaou0kI72NL9GWlkbJxs4+O/jtFQShahHRVkO4\n+256IXTqRJ/btaMpizZ+CUhZAEEQhAhCLG3VFsuiKf9EztT+7B45bhyQmKiX+xJtgiBELuH/FBfR\n5jfR0UCTJjTfti2QnU2FO3kdAKSnV03bBEEQhEpALG3VFra0eRNtDRrQulq1gKuuovf0sWMi2gSh\npiKWthoGi7bUVKBFC7K07dkD5OXR8nXrPF8IgiAIQpgiKf+rLVz3vKSEppyIhImLI8EGkNWte3ea\nd4Y5iGgThJqBiLYaBou2xo1JtP32G1ndNmyg5W+8Qa6T69ZVXRsFQRCEICHukdUKy6IEJICnaHNa\n2uLj9fwTTwBLllC820sv2bcT0SYINQMRbTWMpk3JS6ZFC6BfP7K0cdAzs2MH8OabVdM+QRAEIYiI\ne2S14oUXgM6dgZUrtWhzc49Uyi7CatcG6tQhgVbL0XMT0SYINQMRbTWM5GRg4ULgxhspxb/J8OFk\naWvfHti5s2raJwiCIAQRsbSFhEOHtAgz2bdPJxwBgE8/pWlWlm9Lm+kaWRYi2gShZhD+oi0qSkRb\ngAwYQG4XqanA7NnA3LlUK+b556leTNOmkkVSEAQhIhBLW0jo0IHCDkzWrwcaNQJef10v4wHRfft8\nW9ri4vw/duPGZJlLSQm83YIghA/hL9rE0lYhLr0UuPhiYOZMEnEAiba//qradgmCIAhBQCxtIWHX\nLgo1yMigz4WFwKpVNP/ddzQ9fFjXZNu503ciEjOerSwGDwZWrwaaNSt/+wVBqP6IaBM8YNH2wQfA\nLbdUdWsEQRCEciOWtpDAmnjmTJo2aQJcey3Nx8SQi6RZLNsUbfwTmZa2QERbVJTOLCkIQuQSGaKN\nRxKFoNC0Kb1Mhg0Dpk/3TFQiCIIghAnFxeQ752+AlFAuGjWi6bJl5BaZk6PXxcYC27eTUDv7fxW8\nrgAAIABJREFUbKBVK5rnOm0s3srrHikIQs0g/J/iYmkLOk2b2j9zOQBBEAQhzCgqEitbCGABtnIl\n8P779nUxMVReBwCeeorK7OzapcXaoUPkIlleS5sgCDUDEW2CB1zLjVmyRE6xIAhCWFJcLPFsIaCg\ngAya27cDv/xiXxcdTWKuVi1yY0xLs7tHAjRf3pg2QRBqBiLaBA/69gUuukiPDP7jH1TYUxAEQQgz\nxNJW6VgWibYBA+jzDz/Y10dHU1KSzp0pPb+baDt0SNwjBUHwTWSItmPHqroVEUVCAvDFF0CvXjoM\nYs6cqm2TIAiCUA6KisTSVsmwhaxXL/f1RUXA/v0k1gAgMZHcKXNz9TZ5eeIeKQiCbyJDtImlrdJY\nvhzo2JFGAYuL6cUjCIIghAniHlmp/PorcOaZNN+mDSUdAeyWsvx8ssTVqWNft3ev3iY3V0SbIAi+\nCX/RFh1tf9IJQaV3b2D4cKotc9JJlKTk3XerulWCIAiCX4h7ZKWyaBEJN4BcH7nAddeuepuCAu+i\nrWFDmt+0SUSbIAi+CX/RFhMj7pGVTJcuNF2xgrJe3XOPGDcFQRDCArG0VSp5eXq+bl0tzPi9CZBg\ny893F239+unskmYiEolpEwTBSfiLtuhoEW2VTM+eNB05khKS7N0LXHKJvQ6NIAiCUA0RS1ulcuiQ\nnq9Th4Qb4CnaCgr0OhZkhw9TfFuPHpRdUixtgiD4IvyH30S0VTqdOgGrVwMnnEAjgbVqAV9/Dcyc\nCYweXdWtEwRBELwilrZKxSnaEhJovkULvdwZ02YKsnr1gD59gBkz6HPdurS9iDZBEJyIpU3wix49\nSKzVrUu+9wCwdWuVNkkQBEEoC7G0VSpO98i33wZGjQLOPlsv9+YeCZA4u+46oH9/Krdz9dWe2wiC\nIAAi2oRy0K4d0L49sG1bVbdEEARB8IlY2ioVp6WtQwfgzTd1ghFAW9qc7pEAkJwMnHYasGABsHgx\nMGwYLRdLmyAITiJDtJWU0J8QMlq1AmbNopfNu+8Cjz9uT18sCIIgVAPE0lapmJY2tqQBpJOHDqVT\nn5tLBbjdLG3Jyfb99eoFnH++95pvgiDUXMJ/+C06mqbHjukCKUKl06oVTZcsoT+AXkgPPVR1bRIE\nQRAciKUtIDZvpnfaiBH+bW9a2tiSxsycSWEF331Hn91EW1KS/X8aN6aYcUEQBCeRYWkDxEUyxDRp\n4rmMa9UIgiAI1QSxtAXEW29RTJq/zjtO90gndeoABw7Y1/uytAmCIHhDRJtQLpwDt82bA8uWVU1b\nBEEQBC8UFYmlLQAKCsiV0Uy/7wtv7pHmMu6eeItpEwRB8AcRbUK5uPde4MknAaXo83XXAVlZwKRJ\nwOefV23bBEEQhFKKi8XSFgAs1sxC177w5R7pXMaizvw5nO6RgiAI3ihTtCml3lBK7VFKrfOyfqBS\n6qBSalXp32PBb6YPYmJoKqItpCQkAI88QjXckpKAm24CUlKABx4Arr22qlsnCIIgABBLW4CYoq24\nGJgzhyxvbliWf5Y2X+vF0iYIgr/4Y2l7C8AFZWyzyLKsXqV/EyrerAAQS1uVcuqpwOmnA23aUAD3\n+efTT/HOO1SM2ywLkJ4OjB3r/QUoCIIgBBlJRBIQbGE7epRS9192GU29bVtcrD+XJdrcLHFiaRME\nwV/KFG2WZS0EsD8EbSkfLNr8dUAXgsqrrwKzZ9N8YiJw3nkUEzBiBLB+PfDjj3rb008H/v1vIDOz\natoqCIJQ45BEJAFhWtqKimh+0SL3bU0rG+CujcuytEk9NkEQ/CVYMW2nKKVWK6W+Ukp1C9I+/UMs\nbVWKUpTSmGna1L5+/Xo9v2sXTRcuBDZsqPy2CYIg1HjE0hYQpmjjKkIbN7pva8azATrG28Qtpq2s\n/xEEQXAjGE/ylQBaWZZ1SCk1CMBsAB3cNlRK3QzgZgBo2bJlEA4NEW3VjLQ0PR8bq0UbpzwGdMyb\nuEkKgiBUMmJpCwhTtLEo27TJfVunaHOjLEubIAiCv1TY0mZZVq5lWYdK5+cBiFZKNfSy7XTLsvpZ\nltWvUaNGFT00IaKtWmFa2i6+mETbe+8BEydWXZsEQRBqLGJpCwhTtB0+TPPZ2e5dDHMw0htmen/T\n6paYWP42CoJQM6nwk1wp1QTAbsuyLKVUf5AQzK5wy/xFRFu1wrS09e4NfPKJ92ySktRMEAShkhFL\nW0CYiUhYtAEk5ri7wWzdStNVq4AWLdz3Zy43LW2ZmfYkJoIgCGVRZpdZKTUTwEAADZVSmQAeBxAN\nAJZlvQLgSgC3KaWKAOQDGGpZIXR8E9FWrTCDqps3973t9u1A27aV2x5BEIQajVjaAsLN0sbLTasZ\nAGRk0LRTJ++uj23a6HlzG0lAIghCoJT5JLcsa1gZ618G8HLQWhQoItqqHWecAZxyCtC4se/ttmwR\n0SYIglCpiKUtIHyJNicZGRQS4CtWrUkTPe+W8l8QBMFfgpU9suoQ0VbtWLAAeOYZIDXVvryW42rb\nskXPl5QA//kPkJtb+e0TBEGoMYilLSC8iTa3LkZGRtkDj+Z7TxKRCIJQEcJftMXE0FREW7XDaWkb\nPNj+eft2YO9e4K+/gDlzgDFjgMceC137BEEQIh6xtAUEi7aCAv8sbab7Y1k4Y+IEQRACIfxFm1ja\nqi2maJs2DXjnHV33BgA++4y26dcPyMqiZfn5oW2jIAhCRFMDMz41bw4MGuTftmPH2muleUtEYnYx\nrr6aYtIyM4HWrcs+RoMGNJWabIIgVITwf5KLaKu2xMQAyclATg5w/vn0kouP1y9FLrC9c6d2lUxK\nqpq2CoIgRCQ10D0yK0sPBJbFv/9N0+JiMkj6E9M2a5aeT0kp+xjr1nkv0C0IguAvYmkTKhW2tvG0\nfn337ebPp6n8jIIgCEFE3CP9Ii+PpoHEtAHe32kmaWnAwIEVap4gCIKINqFySU2ljFmcKpnTHDtr\n2vz2G01zckLXNkEQhIinBlraAoG7EJwEyyna2PvDLaYNkNT9giCEjsgRbd6eqEKV0rSpveA2v+Ba\ntqSpmQ4ZENEmCIIQVMTS5hNOw+9NtCUn0+eKWNoEQRCCQfgPv4mlrVrz5JNAdrb+zC84trQ1awbs\n2qXXHzgQurYJgiBEPGJp80m9eiTYWLQ5E5HwwKJY2gRBqGoix9Imoq1a0q4d0L+//hwfT7VqGjak\nz8nJwDXXAAMGAOeea7e0padTAhNTyI0bRxm4iotD0nxBEITwRixtPqlXj6a5uVQvtKiIPj/zDC0r\ny9Imok0QhFAhok0IKfHxQEKCjhNITqZSAIsXkyulKdo+/xz49ltgyRK9bPx4moobpSAI1QWl1AVK\nqY1KqXSl1IM+trtCKWUppfqFrHE1zNJmWYFtz6Lt4EH3bkRCAk29WdrEPVIQhFAhok0IKaNHU4rl\nxET63KABWc6UIgGXnU1FTQFg7VqaPvUUMHOmfT/79oWuzYIgCN5QSkUBmALgQgBdAQxTSnV12a4+\ngLsB/BKyxpWUkIqpQZa2QL0wOKZt3z7g+us91+/cSVOxtAmCUNWEv2jjEUQRbWHByScDI0dq0cau\nJzx/5Ai9BIuKtGj7+Wdg+HD7CKqINkEQqgn9AaRblrXFsqxCAB8AuNRluycATARQELKWsYKpQZY2\njknzF7a0zZ3rOTgYGws88ADNi6VNEISqJvxFm1JkbRPRFlaY7pFMTAxNi4uBhQuB9evt/2N+FtEm\nCEI1oRmAHcbnzNJlx1FK9QHQwrKsL0PZsOMBWjXI0maKK3+6BbGxNN261XPdl18CPXt635dSWvQJ\ngiBUNuEv2gARbWGIm6UtJUXP33svuUlyUW4A+PRTPS+iTRCEcEApVQvA8wDu9WPbm5VSy5VSy/fu\n3Vvxg7Noq0GWNlO05eeXvT0bIzdu9FyXlqYHE90sbfHxJNwEQRBCgYg2oUpo0ICmnEUSoHiC1auB\nIUOAVauoIHd6OmWP7NMHePxxva2INkEQqglZAFoYn5uXLmPqAzgBwHyl1FYAJwP43C0ZiWVZ0y3L\n6mdZVr9GjRpVvGUR7B6Znw9MnOj56g9UtLGudSMtzXfYvMSzCYIQSiLjSS6iLezo0wd49VXgwgv1\nstq1gR49gNdfJ1F38cU6XmD2bCofwD8zi7bt24Hdu4ETT7Tv37JkBFQQhJCwDEAHpVQbkFgbCmA4\nr7Qs6yCA48NTSqn5AO6zLGt5pbcsgt0jX3wRePBBSiRy1116eaCizVfXISlJZyr2ZmkTBEEIFWJp\nE6qEWrWAG2+kmm1O4uOBadOAiy7Sy1q0oMyS8+YBzZuTaFu/HmjViurAcWFUJjGRar8JgiBUJpZl\nFQG4A8A3AH4HMMuyrPVKqQlKqcFV2rgItrTxoNz27fblZiKSilraOGQe0F0MMyGWJCERBCGURMaT\nXERbjaB+fbLMNWpEou1//9Prdu7U9XQAIC+Psk4KgiBUNpZlzQMwz7HsMS/bDgxFmwBEtKWNXeyz\ns+3Lg2Fpa9wY6NiR5p0xbeb+xdImCEIoEUubEHY0bEgukatX62VcS0cQBEEohUUbm4siCPbSqKho\nKyoCLruMLGjsZv/BB8CiRTTvtLSZ+xdLmyAIoSQyRFtsbODFWYSw5eSTgRUrgPffp0BxANi1S683\nX6r9+wNr1oS2fYIgCNUCVhoR6B7JXy0YljYWZuytYbpM1qpFhkrer9nVOOGEwNosCIJQESJDtNWr\nR1WZhRrBmDH0k+fnA+efT8uGDaMC3MXFOnAcAJYto2LegiAINY4ITvnPIsop2kxR5U+3oKhInx4W\nbc4Y6ehoYO1a8vDg/f/3v8AzzwTebkEQhPISGaItLg44dKiqWyGEiAYNgG++IcF22216+cyZ9PKd\nO9e+/apV9uBxQRCEGkEEu0d6E20VsbQNHUrTzp3t28TE0HulVSu9f451EwRBCBWRI9oOH67qVggh\n5NRTga+/JvdHJ3PmeC5zK5wqCIIQ0USwe2SwRJtpaRsyhKxz3brZt2GBdvSotrTFxgbeZkEQhIog\nok2IONxSOC9eHPp2CIIgVCkR7B5ppuA3XSIrYmkDqO6bk1pGT4n3L6JNEIRQI6JNiDgyMuyf4+J0\nJjBBEIQaQw1wjwTs0RHlqdNWlqY198OWPXGPFAQh1ESGaIuPF9FWg/nlF+AxoyJSerp9/XnnAfPn\nS1ybIAg1jBrgHgnYX/8VtbS5Ye6fXe3F0iYIQqiJDNEmiUhqNP37A/feqz8XF9vXX3IJsH07sHRp\naNslCIJQpUSwe6Q/os2fsdyiorJFW0mJnv/jD5qKpU0QhFATOaKtsNA9mEmoESQkAH/+CbRv77nu\niiuoEOu774a+XYIgCFVGDXGPrIhoO3YsME27YgVNxdImCEKoiRzRBoiLZA2nfXtdbNskIQE46yxg\nwYLQt0kQBKHKqMHukfXrl90lsCz/LG1MfLxOaiWiTRCEUCOiTYgoGjSgaYsW9uV9+gC//+5ZbHXN\nGuCEEzzTRguCIIQ9EeweyXoUsL/6ORFJgwZldwnYld7f03PffXpe3CMFQQg1ZYo2pdQbSqk9Sql1\nXtYrpdSLSql0pdQapVSf4DezDES0CaXUq0fTvn3ty3v3priEtWvp865dFAa5YAGwfj0V4BYEQYgo\narB7ZHJy2V2CQE/PkCF6XixtgiCEGn8sbW8BuMDH+gsBdCj9uxnAtIo3K0Di42kqyUhqPPv309RN\ntAHAypXAyy+TG+XVVwPbttHyrVtD1kRBEITQEOHukSy2nKItJsa/SkCBnp7OnfW8iDZBEEJNmaLN\nsqyFAPb72ORSADMsYimAJKWUS2RRJSKWNqGUvXtp2q2bfXnr1kCbNsDkyZT+HwB+/lmLNWdtN6a4\nGPjsM88yAoIgCNWeCHaPLCwkaxpQftEWqKVNKT0v7pGCIISaYMS0NQOww/icWbosdIhoE0oZNw5I\nSgJOOcW+XClgyhTKMPnJJ7QsJwdYtozmvVnafvwR+L//Azp08IyHEwRBqNZEuHskizZnce3KsrQB\nOl5aLG2CIISakA6/KaVuBrlQomXLlsHbsYg2oZRLLiExxnV17rlHrzv1VD1fpw5QUED12wASbQ89\nRJ62Y8fql3hWlv6fbduALl0qtfmCIAjBI8LdIxMSaL6yLW0//aS9OBYvBmbPpsFBQRCEUBKMJ3kW\nADNXX/PSZR5YljUdwHQA6NevnxWEYxMs2q64gvzY2rUL2q6F8KRWLeqvREXpZQkJ9JebSyUA5s3T\n6zZvBpYsofmUFODWW2meX9SAiDZBEMKMCHaPPHaMBt/q1av8mDbTc6NlS+Cuu8rXZkEQhIoQDPfI\nzwGMKM0ieTKAg5Zl7QzCfv2HRRsALFoU0kML1Zfate0xCADQtClNe/fW8yefTNkkmY8/1vNO0SYI\nghA2RLh7pJs4KygA6tatnJg2QRCEqsSflP8zAfwMoJNSKlMp9Xel1K1KqVJbBOYB2AIgHcCrAEZX\nWmu9wdkjAYkOFnxSty5NU1PJKLt7N3D33Xp9r16UqOSbb+jznj2UabJ2bckwKQhCmBHh7pFuou3I\nEbK+xcXRvOXDpyeCT48gCBFImY8qy7KGlbHeAnB70FpUHurXB+64g3K5S9p/wQes6VNTScDVrQt0\n6qTXT5gAjB4NXHghuUzu3Qs0aUJuOGJpEwQhrIhg90h/RJtlAfn5un6nE7G0CYIQTgTDPbJ68MQT\nNJVkJIIP+OXMWccAygzJnHUWBZ0rBbz6Kom2Ro2oZIBY2gRBCCsiWJVwnTZfog3w3SUQS5sgCOFE\n5Ig2fkKLpU3wwUkn0bRRI73M9K6Ni6OUzpdcAkyfTvXbGjWiGLgVK8id0o2iIhJ7K1ZUXtsFQRAC\nIoJViT+WNsC3aItgTSsIQgQSOaItOpoKp4hoE3zw9NPAwoUUu+aLBx4AsrPJ0ta4MXDzzdRJeO01\nz20feYQ6DyNHAtdd57m+oACYNct3bIUgCELQKSoit4FakfOqX7AA+Owz0qMVFW0RrGkFQYhAIutR\n5U+6KKFGEx0NnH665/K//tKjrgAwYABw9tnADz9QPFunTiT0uCwAk5EB/OtfNJ+eTtMNG4CuXfU2\nc+YAQ4cCbdsC/foF9/sIgiB4pago4sxIAwfSND5ei7YjR/T6I0d09khALG2CIEQOkTP8BtBTXCxt\nQjlISyO3SJNZs4D/+z/6A4A2bXQyknfeAQYPBm67zXNfXDJg3jzg22/JYgcAa9dWTtsFQRBcOXYs\nIsxIlgW89BIwaZKuvXnoEIm2unUp2QjDljZ2e2fRVlJCccq8rWUB06bRfAScIkEQagCR9aiKjxdL\nmxA0GjQAPvlEf27dmkoBWBbw4ovA8uXu//fxx8BjjwEXXUSf2RK3bl2lNlcQBMFOUVFEKJIdO3RB\n6+hooLiY5v0RbXl5NP36a3Jz37ABmDwZWLwY+OADvU9BEITqjljaBMFPWrWiDsEll5BgGzmSXHVe\neUVv060bWdRmzdLLDh6k6fr1IW2uIAg1nQhxjzRf6xyHBtBXY9FWVEQi7OhREm0JCbQNi7ajR2m6\naRNN9+zR+4kAXSsIQg0gskRbXBywf78IN6FSaN2apl9+SdMrrwR+/BEYMUJvc/vtQLNmwNVX62U5\nOTQVS5sgCCElQtwjzZg1E9PSNn48MKy0qqwp2nJzacrdAhZxGRl6PxGgawVBqAFElmiLjwd+/ZWK\nbQtCkGnVyv55wACamkHvXbp4WtQ4Di4rCzhwgOaPHQP27dPbcBFYQRCEoBEh7pHeRNuxY/T8LSkh\nl3XGTbTx85Y/b9mity8pCW57BUEQKoPIE22M5FcXgkybNjQdNIisZw0a6HUpKXqamEjukVzA+48/\n9HYs6KZMATp31rEZY8dSR4NdeARBECpMhLhHOkVb3bo0/esvPc9iDKBnaZ06pFfZPZ1FG3s+mJY2\ns26nIAhCdSWyRBubOwBJSCIEncREYOVKSk6SlGRf17ChfXrVVVRLCCBLW/v2NM8ukn/+SVklObPk\npEk03bkzuG2eOxd4773g7lMQhDAhQt0j+XmalaVFm0m9elSeLjHR09KWmUmnJSMDuOIK2rczc7Ag\nCEJ1JLJEm/ly2ru36tohRCy9e9MIrhMWa2xxA6iMANO9OxmCWbRxB2LHDuDUU/V2WVnBbe/gwcC1\n1wZ3n4IghAkR6h554ok0PfNM76INIBdJFm08QFZSAqxYQaKtbVv3/xcEQaiORJZoM4WaiDYhhDRs\nqIu9MqZoS0oCTjhB12pj0fbrr8BPP+nt/vorsOPu2BG40Dt8mEahX3opsP8TBCHMiFD3yHbtgN27\ngYcf9l+07dtHg2exscADDwCFhUCvXpXbbkEQhGASWaJt9249L6JNCCGXXQb8/e/2ZfXr65w4iYnA\nSSeRSCss1KJt1Sr7//gr2goKaDp8ONUeMikpAZ55xn47rF5NQm3jRu2COXmyf8cSBCFMiVD3yLp1\ngcaNqdA2CzQTb6KtQwfK+rtoES3r06fy2iwIghBsIku0nX22nhfRJoSQq64CXnjBc3nfvjRNTCRX\nnvx8YNkyLdpWr6bpunU0AmxazTZsAH74gYQWizSA6gwlJpKFbs0aitEwWbkSeOghEpLMjBk0fe89\nncGS+3KTJwMffVS+7+0vr7yiSyUIghAiwtQ98uhRnaQJ0KKNPRlM61pZljYzEUnDhnpwrV49EnGC\nIAjhQmSJtkcf1ZUzRbQJ1YCePWmqFHD66TS/YIGnpa1pU/ozLW3dugHnnENZJocO1cu/+oqsdf/7\nH40ic6wG8+efNF26VC9jD6ldu/StwX25e+4BhgzxbPv8+VRzzszKVl4mTQJef73i+xEEIQDC1D2y\nTh1g1Cj9+cgRoFYtGqwCyhZtvIwtbZalRduZZ1Iik759yVInCIIQLkSWaIuKoqdxnTrAnj1kohg1\nSvKoC1UGW9r276cOwwknAF98QaILoEszNpZi3pyizWTOHD0/fz5NFy6kqVO0bdjg+f/799N01y4t\nGMsagF+zhtoTaJydG0eOSB06QQg5YegeyRa2d9/Vy44cIcsYJ4j2V7QlJpKl7cAB2m9KCom/r7/W\n3geCIAjhQmSJNoBMGo0akTnhjjuAt98GFi+u6lYJNZShQylY/uGH6fMZZwA//2zfJi2NLttOncjl\ncdEi9zKDOTlkRVuwgD7zfgoKgHfe0S6Uv//u+b/bt9N0927/RRtb2IJhaTt82D/RtmSJ3S1KEIQK\nEIbukYcOeS5j0cZuj95E2ymn0JQz/CYkUFeAa2pylt927YDWrYPabEEQhEon8kQboEUbs3Gjff3i\nxToSWRAqkeho4KmngNRU+nzmmXodu+Y0aULTiROpIPd//kOGYicjRgAdO+risGbnZsQIEojvv0+i\nbfBg+/+yaNu+XYs2y7KLQ6c4y8tzXx4oluWfpW3RIuC004B//7tixxMEoZQwdI90e974K9q+/BL4\n5htdLNupV1m0CYIghCORLdrY9LBypX396aeTyUMQQsxZZ+n5W2+l6ZYtNG3YkFJSZ2YC6eme//vF\nF3rebZR4zhzgmmvIGtelCxmZO3akddu20XTXLioTAJDbkCmknKUDgmVpO3aMrGfODHBOOBaPw1IF\nQaggYegeaT5vlCL3bH9FW3IycN55+rNzoEhEmyAI4UxkizbuBf72m/t2bj5oglCJNGwIbN4MTJlC\nFrUbbgCef16vb96cLtt//MP3fnr3pmnjxp7rjh0DunYl69uKFbTMFEzsPrlzJ2WnZJxZKNnSNnEi\n8MYbZX83bxw+TNOyLG3cRrcU3oIglIMwdI90DhItWULPDn9Em5NHHgEuukh/FtEmCEI4E7miLSuL\nAnjq1KEUfewfZrJrV+jbJtR42rYFRo8m98jXXyfrGNO8OSUNWbaMRpkB2u7DDykOY/x4sqCxWBsw\nwP0YXbrQNC7O0ztq/XqaFhTY3Sidoo07T7/+6lmDzh9KSoBvv9VunCLaBCHEVAP3yGPH6Nn12Wfu\n62++Gbj3XvIKOOEEz8RHJSX+W9qcpKQAY8fqzyLaBEEIZyJXtB07RvNPPUUWtVdfpbR7tYyvLH5Y\nQjWjeXM9z+6KDRtSSv70dOCxx8iCxjEb3kRb5840VUoH4TPexNN//0uj0tdeCzz+uLa0lfV/APDL\nL1TegDNaAsDnnwPnnw+MG0efy3KPdNaP88bmzcD06b63ccOyyAJZEauhIIQV1cA9cu9ecgG/7TbP\ndTt20Kv5+efJw2D9es/st+xa7U20laVJ09L0fP365fsOgiAI1YHIFW3MOedQINGcOcBzz9ldIquj\naDt2jKo0c054oUZhirYmTahDYl7ODFvaOFuacx9m54Q7LW3b6mWcOtvkl1+AefOoAPeECZ5uSmyh\nAzw9i4cNoxIBZhptFp0sksqytHHylcOHKd6Oi+I6GTYMuOUWitM791xKPOAPeXnkGloeq6EgeEMp\ndYFSaqNSKl0p9aDL+nuUUhuUUmuUUj8opVqFrHHVwD2ypISmtVx6G++847nM6QCTm6tFG4s1X9Y1\nJ5zoCdDeC4IgCOFI5Iu29u0p6ci6dfQCM+GYt+rE4sUU0MTFuIQahSnalCIrm5tLz8UXk3Dp39++\nvFYt4Oqr3fdpijZOUML83/95HsMp2j77jGLwCgroOLffTstLSrT3sVnOgJOfMEeP6g6cGyzaDh2i\nunVmZ8uE+6CffQZ8/z1wwQXasO6LYNSbCxdmzrSLbKFyUEpFAZgC4EIAXQEMU0p1dWz2G4B+lmX1\nAPAxgNDlR60G7pH82nUrZL15s+cyp2g7cMC3pa0s4uP931YQBKE6E9mirVkzesoPGECmgTVr7Ns5\n0+VVB9hHjPO6CzWKFi1oevnlNO3dG+jVy3O7Nm2AV14BYmLIMnX33bT89deBZ5+1b8uizRRB3bvb\nt7nzTi3CGKfo+te/gDff1JatqVPJBfKii8iFqUkTcm16+mngySeBjAzPdnuztv34IzDUs6ssAAAg\nAElEQVR3Ls1zDBwnf3XSoQNNv/pKL/vlFz1fVOQZnwdo0caiLz/f+zG8UVQE9OkDfPJJYP8XSiwL\nGD4c6NGjqltSI+gPIN2yrC2WZRUC+ADApeYGlmX9aFkWOwcvBdAcoaIauEfyPZaZSaVTzYEbN4cS\np2jLyamYaBMEQYgUIlu0ce/upJN07mAmLo4+Z2VRcZdDh6gn/L//hb69JuwT5s03TIhokpNJgLz7\nLn3+8ktg8mTf/1OvnnaBdLNONWtGU3PA/aGHgNde05/T0oCXXwYGDtTLTOuVadH79ls9f+mlwNdf\n0/x119H04YeBRx91N2R7E21mKQS3nEEmPHJvtmP/fj0/Ywbd+s5baOdOmvJ5aNKEbvlA2L2bktFe\neWVg/1cW774bvHEaHvfxZdUUgkYzADuMz5mly7zxdwBf+VgfXI4dq3JL29Gjen7KFF3ixLmO4fuU\nycmh+zspiQazxo71/EqTJ9MAkjemTSPrsyAIQjhTM0Rb/frahMH07EmirX9/8jXLygK2bgVWrw5p\nUz1gnzTueQk1jv79A8+gyDFuXMTbhC1tprtj27b2rJUc43bttTTG4WTiRJ305CsvXc7zz7eLr9Wr\ndWkCpqy4NkCXKQA8PZoBXULAxLxdfv+dRvd/+cUu5njMJiaGprm5gSeQNfdX0YohO3YAH31ECWau\nu87+e1QESYpbPVFKXQugH4BJPra5WSm1XCm1fO/evRU/aGEhEBtb8f1UAKc12xxMMEVbcjJNndfv\nli30NdLSgBNPBJ55xvMYY8YAl1zivQ233goMHRpYuwVBEKobkSnaEhPpCX6p4aXiHFLv3p16cdyT\n4xdkRSsJM8XF7j3OshBLm1AOLruM3Bd79vRcx0LOvKRiYqgaBsNxH3//u3s4ZdeuVC+pYUN3t0eA\nhONLLwH336+XOWPufviBQky5s5adTZkgGzSgTlvXrvbbZt8+z+Ow+6SJaaXiW/r88+2xe7y8sDBw\nt0jGFG3PPw+8+GJg/19cTFa6Dz8ky+bQofr7BCsvEnd6JelCSMgCYI4INi9dZkMpdQ6ARwAMtizL\nxb5EWJY13bKsfpZl9WvkloEoUAoL9ShFFeG814qL9XxhIVnQAB1z6xzY4WySZhZIQRCEmkhkijal\ndLAN4xRt7dvbe3/sk1ER0WZZVEQrP596i63KkSRMLG1COUhOJpdHtwxt3Ck6cABYvtzuVsiY2STr\n1KG6SiYpKTT15U7YuDGJrn//m0TkxRfbayQBwPXXA4sWUV4ggITVLbeQGBo92jMOi5OTmHiztO3b\nR7e0Gaqana3nWbTl59uTdJidyLIwRdt99+lYQuaFF3yXFHjrLYqHGz+eYnxKSnT8nT/JVPyBRZtb\nhlAh6CwD0EEp1UYpFQNgKACbo55SqjeA/4IEm8sVXUlYVrUQbU4XSPM6P3oUaN2a5p3PHIZfzSLa\nBEGo6USmaHOD3wzDh5PfVNOm9vXc03EWpwqEdeuAUaMoo8KWLeVLVyeiTQgyffqQiHr1VaBvX0qT\n78QZI+KM2eLMb3ybnHkmTU2vYxZ2AGV2nDtX33bO/ezYQRY10xUyMdEz05sp2ubOBVau9LS01a9P\nlrazz6bb25lfKCqK4tDM5CS//qrnTSFWFqYIZEx3r3/8w3dJAS6J0K2bbueO0ogot/geb2za5C5e\nARFtocSyrCIAdwD4BsDvAGZZlrVeKTVBKcWl6ycBiAfwkVJqlVLKR/RVECkqIuFWzSxt5uejR+m5\ncfnl7hlsTUS0CYJQ0/FLtPlRh2aUUmpv6QtplVLqxuA3tYKwq0m9euSz5U20VcTSxj06sxcYqItk\nsNwj168n/62iIuCqq4BVqyq2PyFsiY0lEeWWhdIbjz4K3HMP3RbmOMadd+r1ANCpk17nlqTO6aLH\n4vDyy4HBg+3rkpK00GBXKfZazsig7fv2pbERM3YvOZnauW4dCbutW+37LSkhC9iyZTrM1cw2uWcP\nxZYNHky3iq/C3Xxrm6UZNmwg8frHH97/j+EkK3l5nqLN39KMRUV03q+6yn09P8rcUqwLwceyrHmW\nZXW0LKudZVlPlS57zLKsz0vnz7EsK9WyrF6lf4N97zFI8AVVRkzbBx9UbhykL9HGIXeffgoMGaKX\nN2jguR8RbYIg1HTKFG1+1qEBgA+Nl9JrLuurlsREmvJwdjNHgq9guEey0DKtZG65x30RLEtbz57A\nvffS8T/+WOq+CQERF0e16FNT7dav++8n0XDmmcADD5C7XyCYHTZnQhNTtHEyFLa0vfCCfVvTlSo5\nmQQZW7zcxkkWLCC3LLaCLVum1+3ZQx3GuXPpVrnlFu8uk9nZZLjo108ve/llus2mTdPL3FwdS0q0\nUDt4UM+zkOM+9t69dmtiQQGJXz4HPDb01VeUgMH5fYPhNCBEAHxB+bC07d9PxeovvdTrJhXGaUE2\nY9aOHtXNU0onYGKXbhOptyYIQk3HH0tbmXVowoLLLyffqSefpM/O4JxgWNrcrGTesjYEso/ywL1O\nzuRw5Ij3bYUay4wZwCOPBPY/UVFkVZs4kcY+PvrIdxyXvyQlabHTtSsd56uvyEj+4ouUtIM7eKZo\nS0rSacRNMWWybZvOTwTYk364Jen73//IhfKHH+zL9+8nd64uXfQyHu8xLY1untF79ujvt2OH7ryy\npa2ggLzZGje2W0XZgvfcc3o/zH/+A/z0E81/9x310zle8NAhzwyXM2aQlTCQOD4hTCkVbcVRMfjk\nE/dsp2w5dlqnmdWrKRtrRfDH0sZw/bX69Wl6wgkVO7YgCEIk4Y9o87cOzRVKqTVKqY+VUi1c1lct\n9eoB770HtGxJn2vXtvtg+BJtRUX+BZy4WdoCFW3BjmnjHqmINsGF664rHceYNcuzQJKfXHklJRgp\nD+YtmJSkBUlqKrlDffONHne49lrKXgnYc/zwqLxSwH//SzmAJkzQHtFMy5baK7qoiHIRAcDVV+tt\nWHj9+itZ+845hzqQt91GacP37KE2X3MNcPrptC2fNtO9cYf5xHQsa9DAHnfHy4uLda27zZtJkBUW\nAmvX0jKuwecUmUeOUHHy886jWLmVK+m7WZZn3NvatXRs52OusFDqukUcpe+sb36MwZVXkhXZCd9b\n3lxpe/WiAZSKUFZMm5shkGNmJ0ygUiO+0vkLgiDUFIKViGQugNaWZfUA8B2At902CnoNmopiukh6\nE20lJdQTcite5YSFlhnT5ibaPv0UOO0096HPYFjazP2KpU0oi7w8Ui4XXlgpu583T8fAAeSi+NRT\nNG8mKklK0i5QrVvbhVn79pRAhUfgeRodres7tW5NSVc2bqTjOV03GzcmaxuP7HfubF9/4YWUgbNp\nU3th8EOHgFdeIUH4+eckurp10wXQ+dFhun25FQhnT2lnJ9jc9r339PyYMVSMmEUbf09nRs3sbPrO\nANV8S0ykhCgAWSTNsgn8iDIfL0eO0Dm54w7PNi9fDlxwQdkFz4VqSOkowt5cUkVuYzJlibZgUJZ7\npGlp4227daPX2OWXA4sX+y6cLQiCUFPwR7SVWYfGsqxso/bMawD6uu0o6DVoKorpY8VvNDMQZN06\neptt2kR+Itdfb08754R7Qubb0c1PaskS+nOz6vGyvLzy+zCZUeVOS5tlUWCSmfM8lGzbBixdWjXH\nFtzhXpQ3H6kKcuGFlNSE6dKFUv0DdmGWmAg8/TTw+utk4eJ1HTqQiIqJ0TEvcXEUl5aerpObDBpk\nP66ZzRIgy5tSOolJWhqVJJg+nUTk55/TyD4fzxscd8ePMLaUmWM1biKHt+vWzb68uNh7rojdu7Vo\ny86m/TrHu3butGe1TEoCEhJofs8e4J13dB4irmdnPnomTqSpKRiZf/6ThJtbjJFQzSkVbVY0XVxu\nrxMWbW5JhIJFIO6RbK3mAQpA6g0KgiAw/og2f+rQmHmdBoNSH1d/pk4lnyKT3FxtqTJTzAE0dO8r\nYptFm+n7tHu353bcc3LmDy8poeNz0hS3/OL+wAE+gB6WZ9GWnQ08+yxw4onl23dFsCwyh5xySvCK\nUlWEoiJK1lKe0gyRBPvQuRV5M1m9mizObtWty4AtYwCJHQ4pNS1tiYm03Q03UEeNRRt7NANaMMXH\nU/xay5Zaa15xhf2Y7ErJNG5MU3YzbNwYeOYZ4KabgIcf1h3Xjh2Bn3+2/+/JJ9N4zfXXAzeW5sat\nW9eeVt+8jLZsodO0dSswciRl78zMpA4qZ8Y0OeUUqt/mpLBQx7StXEnnxDnesnOn3ZqWkGA/3/fc\nA/TuTfP86DEtbcuX05S3YX77jdxTx47VIlAII46LNrK07d7tWQ7Dm6WtqCh443qBuEe6iTZBEASB\nKFO0+VmH5i6l1Hql1GoAdwEYVVkNDippaVQM26SoSL9V0tOpJ2emrvOVPpl7QmbvzU20sY+SU5Tl\n5ZGw4d5ToPFwjHl8p3skd9BNH5VQsWCBnn/00crNM+0PCxZQWYTbbqvadlQ1fG2UJdruvpuUi3Mw\nww+ionTCkwYNqFM2YQIJGsZ5a7GgcxNtpliaNAm46y7gjDPs/++0tLFoY0sbf3bSoYOn5/I339A4\nxxtv2MWhKQzNDvGmTcDAgSROZ8ygGlQ7dlASEB6TMTumycnu1qzcXM/4ODfRZlr2nKLNxE208W3o\n1OKzZtHjz1fdOaEa4xBtEyfaS1UA+hXktMLde2/wkoA43SOdos3tlSqWXUEQBE/8imnzow7NQ5Zl\ndbMsq6dlWX+zLMuPikXVBE5XZcK+Q+npNCzOQ/MAUKeO932xGOO3UlxcYJY2dqs87TR9/PJg9sic\n7pFmz6yiGSoDxcyzPnEi8K9/VXyf771HPd7yWO64Z16dc6PffjtZRisTfy1tfH0HUrX544/JPHTs\nGCZMAL7+mtwllSLd3rMnxZGZbpKMm6WN3SNNUdWzJyXtcFoLEhPtYXpOS5s3D20zMyQf05sIMouL\nc8xa9+7kXmkWDuf1zZtrq1Xz5vpUehNtK1ZQh9r05HZm85s5k8JkGW+i7cAB/ROa7pH8iDIfDZZF\nlr+//c29ZpYQBpSqJRZtbvCYnjPvlTNpSUWyjTotbTxeaFn02HYTbWJpEwRB8KQSPdnDBO4FAuSn\nUVhIPZrUVBJN7dvbh9NjYykAZsEC4Ntv7ftyiqC2bSlDgGXZHfNZtJlBMIC2kJ16Km3Pou3oUarg\n6/Rf8obZDqelzeyZrVql03SFgv37KQDprLPIdBGMYIXRo+n3ys31NK24UVREfl8nnqiHgCvDVfPB\nB8lC6+yxBMq335KKuO++4LTLDRZt5u/x4Yfks2cqJu7Z+ZNJlbn5Zrre9+1DrbS047FsJv/6l7t+\n5+yO7duD/Avz81Gv3nAA/uXVUYqSoJx+OiUzYJFWlqXN6TGdlub9Uu3enfZt0q8f8OabntsuXUpl\nC1i0NWtGWSIBsu65iTaORevfX29bltd0QoK7/t62zdPSVlKiPajNR8PmzSQ8777b97GEaozD0saY\n1i1+PRw8SNcCXzdOJ4i8vPJbv7xZ2nyVkRNLmyCEF8eOHUNmZiYKKtrniXDq1KmD5s2bI5qD8QNE\nRFt0NL2pSkqod7hhA73BLItE0xln2IfkY2OBRYuAhQs9xZibaFu7lpbPmkW9onvusVva8vIosObh\nh7Voa9uWhvC5l/bEEyQU1661+6x0705tX73aflw3Sxt3zM0c4FlZ1BZfw5rffkvn6G9/876Nv3CR\nq6++op5zIJ1/b3Dv3dzX8uUUVDRkCAmyP/6gcwVQ53/IEEqvxwXH3aoxVxTO7mBZ9Bs88wz9+Sh0\n60p+fsWFX1k4LW1Hj5K6aNtWX4OAFm2BZCLlnlluLqmfAGjXjgRR//4AzpwE5Ofj+meH4733KMbM\nX1iTs6Y3Y9rciImhn2/KFHI79NXsHj08l/Xt6y7aiorslrZmzfSp7NrVd0fVWVbSyaOPUhKXoiLa\nf8+elA1y4UJgzRraJiPDMzltTo4+P6Zo++47mjoFrBBGsGiLsZuydu/WYzEs2iyLbtGkJLoOnOUf\neF0gHDlCY6LeYtr40SDukYIQ/mRmZqJ+/fpo3bo1lGQPcsWyLGRnZyMzMxNtynqpeyFYKf/DG35D\n9S1Nerl7NwmqQ4eo42qKtsJCCjI5epSGzk2x4CbaeH+33EKBAoBdtL3+OpkZnn9ei7a0NBKQbGnj\n2DYzJgyg7JbcI9uxg3JzP/KI/+6R11xj93168klPa94jjwCPPw6/yc2lnsCKFZ5xc9nZdDylKJOE\ns4hUeWDBZfYMnnuOgpwAqvzcu7ceOmYX1MmTyeJm7qMyyMsDHniAjjd3buD/f+RI5ccfOmPa2JSz\nbZt9O76uePsdO4CrrtK/45EjnsFg3DNjwffbb+6lLrxw6qml2SF37AByc3H22fTvnTr5vYvjCUbY\ns/miiyhOy7aPr74iq2ApDzxAXz8pSdd2c4PHAkzYu3nECOqQchkDgMZiWLSZ++3aVce6uXHBBfbP\nHTvS9Kmn6NROmKBj+hIS6Du/9BIV3l64kJavW6dPPf+U7BrZrh0tu/9+Glv57jtyT2VrpxCGlN57\nx5R9oMhMbmxmImUB7+Za65bo2BfffEOuvz//7N09kl+dpmjjMhyVWYJAEITgU1BQgJSUFBFsPlBK\nISUlpULWSBFtJv360fSvv/TbLDXV7h554IDODDBgAPX8jh4lp/+cHHtPzBRtzPbtOoYqO1sPc69a\nRcetX5/+2rXToo2P7/TDYkpKgBdfpDfl229T74uDZZzWEbfMf9yTe/RRaofpLrh/vz01HQCMGwd8\n/z3NT5hA6feYNm1I5PbrdzyWybYvFolxccGtHbdihc4EsW8f/Ravvkq9huJivc6taHllZrLMydEB\nIYH2fADvou3bb+lclmefTpyWNhZt3vKA8+/24IMU/PLZZ3TO4+Jo8MGEz+3Bg8CcOVRIzS23vC+K\niujeyMkhf70AE/S8/TZZnfr0oc+tWwOvveYweg4aRNeLw8Qwbpz98nbiTNZQuzZZ37Zvp2SzHTpQ\nR5Rv4ebNtcXPzJzZpg11VM0sjTwQV6cOuXiaHWwWi6mpWuzx/5qd7rg4EpF16ugskYC+bPjRxOLs\n2WeBadPotjnjDEm3Hs4cPUSiraDEu2jbtUvHZZqvNieBhj9//TVNf/rJu3skLzfvwyVLaHBBEITw\nQwRb2VT0HIloM2FLm5lDu2FDGupnf41t2+yJK7Zto2HpxYvpLXTNNXodZw8w61/97396PjtbvyU/\n/ZSyKbDoa9+eemlKkSADqKPOxzY78n/9pXtf0dH0hjWTpwC+RZszts60sOzfT+3MzyerVV4eCbUp\nU2j9449TD5g7/ua+9u615043RVu9evQ/O3fae5MADfezpcxfhgyhHvHhw/TbFRaS5eTll2k9B+64\n9T4qMxFJTo6Om/RlWSwu9hRgxcV0TbmNytx9N+3bV0Exf/FHtJk9L76WeH1RkbZkTp3qfowDB3Ta\nw0Bzie/cSWIqJ4fuheuvD+jf27Ujq5Nfo/eO++Puu6lmnDcSE2m84IYb6HNCAt2yLVrQdNIkcls8\n+2xa36gRXaY//ECPCrbCcdtMt7AhQ6gDu2QJfTbHjk46iabNmullLNac6fmVojDSOXP0MjdLG/PS\nS/RzssgVwpMXnqF7Nr/YXbTl59N1wMXe9+/3Ph6SmwvceaddxE+bRhZfN8M5Oy/Urh2YeyQXrhcE\nQQgl48aNQ7NmzdCrVy907twZt912G0pKB3FHjRqFZs2a4WhpP2jfvn1obY66hhARbSZt2lDP6Ouv\ndTo27im5FVdidu6kVGt16lCsFNOnDwUPjBunl5nJS5xp3wAt4tz8kvbvJ2EH2CPFMzLsacA4kYqJ\nM+W/ibN4D1v4iotpf/v3A8OHUy/yvffoLb1ypb0jP3++u9XHtNJxTBtAJoDDh8k11FkVeeFCz/Rl\n/vLaa56WQUCLNjdLm9v2gZCZSb+lW+8lJ0cPJe/aRaLGLWX+I4+QAjCtjyzM3Sxt3PP66SfPmEaA\nvu8dd/jnWulMROIm2szheW4jKw1TcHoTwAcO6J5coL5PzuF/buedd3qW7Kgo5bBc9umjx1o6dLCv\nu+ACEn3Tp9M4B8finXUWdVYzMuwVOli01aoFtK/3F7p1cxdP559P3tlmzJk30QaQx7AJizb+WU3R\nxkJORFt4k1CHVJE30cavEC7ZuX27Fm233mrfV26uHv/i23jJEhozchZ7B7SB3Zdoc3OPFARBqCr+\n8Y9/YNWqVdiwYQPWrl2LBUZIUlRUFN54440qbB0hos0kNZXiyX7+WddmY9E2b5732K7du0l8nX++\nPWtBYiK5kJnDlzNn2v83K4uCbLgXdfXVNDV7UQDt++STtegzRdsZZ+jh+IMHqYOclGQvT8DxRmxJ\nMNvJoo2z2bBoY4FTVATMnk3zP/xA0+3b9TEB4MsvKVGKE47fA3RMG6DdI9kV1Ywry8vzrBjsL+vW\n+RZtbpa23Fw97BsIkyZRT7xFCxK1HCNnnpecHH3M7dsphb9bFg2+LjIy6DxdeaUWK26WNt7nXXcB\nvXp5rh8/nto2a1bZ38PppsrnzxRtposvC0Fef/SotrCa59c8pwcOaDdRb26X5vHNfPlO0cbX9fvv\n03UXTMppdeXTc3z8oajI5ueVkECJTp2eEQ0b2m/FpCR6DK185Vfc+Hgzz7oBpbRoQdY2M0skizU3\n0da5Mz3Whg+ny4V/ps2b6TFlJglld0u3y0oIH1i0HS6yqyIWbTzt35+uoy1b6PETFUUF1U3MsQy+\n1fm15mad48e5ZXm6R/Ljw1f2SEEQhEDYunUrunTpgptuugndunXDeeedh/zSh82qVatw8skno0eP\nHrj88suRY/ZLXSgsLERBQQGSjSR9Y8aMweTJk1FUmTkQ/EBEm0lUlN0HCdCWodRU92JS9euToMvK\nogq6JrGxwCWX6M/jx+v5qVP1EGdcHPW08vKA//6XljlFW0oKWQJZYDlzMvNb1bLI8pOYaC9nUFJC\nb8lDh6hdZsZIp2ibNIl6oW4XNseyAVQ1GKCU9B9/TNY3J7yPggISB073SBYIplslfxc3EWjiFou2\nerW7yOFetZulDSChNGkS8OOPvo9p8sADZM1ivv+eRC1nogDo+/MxfRWl5uvszz/J1faTT3RgiNNa\n5s9Dg//HNON4gy1tfN7Y0mZaxFj0Ap6WtoMH9e9sCjVTPJuizS0f/csvU+VqgO4Lji8F3EVbSQnt\ns4yHr1+Y57OcMYLc3OHDSxdMmkSBZyzk/SQlhW7NnoWlNQ29BPi4ZdezWdqOHKHBHMMKe/LJZChv\n1Youi1GjSPd27GiPg7v1VkoA4yb+hPChfizdi0eK7KqIvd9ZtLVoQX8ZGfTXsqU9eQ5gvy34tvZH\ntB06JJY2QahpjBkDDBwY3L8xY8o+7p9//onbb78d69evR1JSEj755BMAwIgRIzBx4kSsWbMG3bt3\nx3izL24wefJk9OrVC2lpaejYsSN6GSOXLVu2xGmnnYZ33nkn4PMRTES0AeSKyKP/TjFkFt82hc6n\nn1IHv3FjGsKuXRu4+GJax1kClKIAFmbkSBJlV1wBXHstBY8AWlzFx2vh5HxrpqRQAEtWFgkzfuPe\ndpvehv9nzx5P0QZQR+7QIc99b91KgTfcGd++nYbxP/8cHhw4oPOcz55NHfdx48ha5nYxc6eap6al\nzRRtZvEptnawaCsqIl8wFnb5+bSNW4fdLOBt4svSxusfeID81jZtco/Nsiz6zbZt8/QJio0l0eas\nfJyTo9u5aZNePnkyZbVkl0oWbenpWmj9UVqjnns5u3dTtlC3XtKTT9qFFYvUsoQvoEUbCz3+Lcwh\ncjfRxqJ55Up77CILN/N/Dh7U/8fHWbpUn5s776SUjoCOATWzVJrExuq85MEQbaYVsZyi7aab6NY4\n7tXMsYY//RTQfsaPLx234d/ekcHzu+8oWYhHLHNeHhLiSBQnJJQed9Eiu7t2KQMHkoXt7beB+jnb\nMBpTbY+Ep5/2nvNICB/iY+g+3H9Iizal9OODXyFpaTQemJFBl22bNvbXHmB/bKanUwIe/v+1aymp\njhmuzY8sN9F2+DC9It9/nz6LpU0QhGDQpk2b40Krb9++2Lp1Kw4ePIgDBw7gzNKaxCNHjsRCTqns\ngN0j9+zZg8OHD+ODDz6wrX/ooYcwadKk47FuVYHUaQOoNhv/CL5qlrFb1i23AJdfTvONGlEPqFs3\nLUiWLHF39E9Lo+QYnFq8f396c3krhjRxIokEtpylpGhXtF27yGLx0ktk6du2jaxzPLKemOj55j1y\nhN6YcXHa6gFQ3nBm7FjKiPnPf+oSBU66daO3+8GDFOt37rm03EwoUrs2tYGtTCwEnDFt3MHn9Zal\nRRuXMxg2jCx5N9xAJRL69iVx5BRISUneLWm+YtoAe0KPU06hczxiBAncZ5+l5CjffEPuiN9/73lu\nrr6aRK6znp1paWPq1aN6fQCdQ9Ns8uefusQEd9zz8+m8nHEGCT+zd8Q8+ihlcWR3Ov5ft3g3gK73\nr78GLrxQiyOe8m+Rm6sr7vL5i4/X2/Hv5Iw/zMyk68K0vJoi+NAhuj9OOQUYPNieIcNk3z4a9neK\ntpgYLdYOHCDxaKbBCxTTGnnffXQ/8ACMnyjlSNnPGUI2bgxoP8dLCEwovbYdou2cc1wSo+TnAwkJ\n+Nv59wB4jqxmuaX3t0v84LBh9DWLiy1sQ2tgGbC+aAiAhse/ixD+xEfToEvWXq2KevSg111xMYmu\nqCh63LRpo2sLPvGE56vDdISYPdueS+i77+gyHTNGP7L5kZeX52ns37KFHntsRBZLmyBEFhxdFGpi\njYdJVFTUcffIQImOjsYFF1yAhQsXYujQoceXd+jQAb169cIsf8JOKgmxtAFk3eIf+4MPqAPsxrnn\nkmjgZCCA7mBzgRmAfI3MxCXLlpEAcw4pKkU9KBYyTh54gAQiQNYm7ghmZVHHOGzqO5IAAB55SURB\nVDWV3ro8TG4mL0lM1CKT/ZxMS5sp2kz69KHsfJ995r4eoONy5pw2behYStkTcTRsSAKYO9fsgskV\njevVo7awQMjOJoGRm6v3s3YtCUEWBRwUymLNLPwMUEVhb5RlaTPd2LiH0rs3mT74euCYxtxcu2BM\nTCShe+AA/S4mbGkzf3szhozFPZ+nP//UvRyzw19YqC11znp9zMqVtO+CAh2c8vvvwBdfAI89RnFz\n119PImfqVIqlnDVLW9qKiujPFNAcA7l3L103DRt6ijYnv/9O//vcc9RLdCbxycvT1ZtXr/bunjhx\nIv0uO3bYayUeO2a34E6dSvdfebOAmj3QNWvIpdlb7nN/4fPm5jLsD7+7izZXSuv/dd7wCcaNK80G\nyFZQF9GWmkrjEBemaKt0wpFdHtsJ4U29aBoQy9oTfXxZjx50aWRlkWhLTaUxGbPO66hRnh7Mpqcz\nhzUD9NjfsoXmzduPb09OtmwmM3Y+gkW0CYJQWSQmJiI5ORmLFi0CALzzzjvHrW7esCwLS5YsQTtn\nmBKARx55BM8++2yltNUfRLQ5adGCkoe4ERVF1h7zLcOCyBRtTvr1IwFWHnjIMz/fLto2b9Zxb+wG\naV5giYm6k8+WsGuuoU6xKdrOP99+PBaQblWDWXg0bqzj+9q2pTc8Wyi5g87BOfz2ZosP7zcujnoP\n3I6pUynzAcf0xcUBv/4KXHYZCYV//pO+sxlTd/HF9iHh00/3bDOzZw9tb1pAb7iBiioDVJ/OSXo6\nuX6yf8/SpTSdP9+eXq1ZM50+0OmeyaLNKNzs0S7eDiArbenDxSP5B/d8vFmmABIgmZnalbN7dxIh\nTzxBAVdvvQW8+y59Bz6+2YvKz7f30Hjdnj30u9erp90bnSJJKfpbvpxcbDdsoO/NvTomL4+slgDd\nP+bxTBfSqVNpECEjw54H/MgRPZR/4AD9LkeOaPG1cSPdbx9+qAXuggXuw38jRpCQNXPnA3SOKgKL\nzzVr6Fyb38uf/2Xh7o9oK01iE9W1Mx5/vLTDzb+NKdo++eR4u8aMAd59eMPxVfGHSLSJq1rkUK92\nIY4iBrt2a9Mpe7ZnZJBo40fK1VfT6+G55+we/QCNaZqPze3b6fF8332UHZXHeMzHAd+e7J1tK2Tv\nQK45QRAqk7fffhv3338/evTogVWrVuGxxx5z3Y5j2k444QQUFxdj9OjRHtt069YNfaowtbKINjdY\nBJWV5Q7QnVpfb6WKwG6Yo0Z5ijYWSFxI23QPa95cxyRxEally2gE33SPNEy/ALRoc/rHAHr03kzK\nwkO0LNpatdIJXZyirWVLe0ybCWfFZDdFzrCYlUXrLriAPrMAZT78UM/37g1cdZVnuwFyn3NmGpwy\nhfbbsKH/CSPM7AwXXkjTpk29i7bdu0lQpKaSUHG6T+7eTUk4tmyhRDa1a+vsnSavv66zRbgJTGbb\nNm0pbNGCTCpOpkzRVkp292RYtPG1b4q2Ro3ouvj4YxJ/zvgvy6KkNMuW6e/ARaBMtmzRpS62bLGL\nU7e6c9nZ9irWR47o66qkRMeNsb/V2LGUCGToUArg4u/8yCN2a3BRkY7DZMsfY8bouWG6vP7xB4nV\nxx8HHn6YlvF5y8uj37xTJ7oH/Ckoz66tHTtSD7ks/3m+ds2eNbePf8f0dMpIys8CAA1ytx6fr5dH\nv8FFF5XdPCE8qBtViELE2HID8ZhZRgY9EjlzaceONE7BXtsmzZt7OjUMGkS3mJm369Ah2sfEifr2\n5PGajh1pyqVQTcTSJghCRWndujXWGYm77rvvPowrLbfVq1cvLF26FGvWrMHs2bNtWSGZcePGISsr\nC6tWrcL69esxc+ZM1C3tB7/11lu48sorj2/76aefYqtZfzmEiGjzxs8/+zc6zlYY7rQHm7ZtqaPZ\nqxe9YZWijm1WlrassVugOUTat68WVoMGaetMZiZZ2jgrzrnnakEEOAJzSrn1VhI83OE1LW1O0Zac\nTMIvJYVitdavp79Vq+w5xJ1JUhh2oxwwQH+33r0p/m/ECM/tzXjAuDgScWYCDMbpDlqvnnYfbdbM\nXofMF9yrfeop4Pnnad4UbU64A56cTG299lr7+rlzKQkHQALH7TsCNKxtChq33wmgTj73mJKTKQDq\nuuvs2/z2mw4+cZKfT+ePC465WdoAGpJ3c0c88US7aGvXjiyHXF0aoGvhyBEqf3D4MH03xtk7ZExL\n265d2lLI3xnQos0sdcGkp9stdIAWOdOmkdhkOnak+9+t7h4fJyVFn0POojphAonkoiJ3N9xevega\nLUuE8TVz7rnkFuur9EVBgf7+mZnacszHZ0sbXxNmYpqtW4+7Vsfm7MK6dZRdUogM6qqjKITdjMVj\nKJmZ9Mgzy014o1kzT4Mvjz2ZY1hHj9Kj5sEHPUO6+XHiVu1ERJsgCIJ/iGjzxskn2x39vTFlCqWO\nY5FRmcTEUCeYLUYs2iZOJMsFW34AEhG//aatGKecotfFx5OF4Ycf6I381Vf0lp02zf6d09Np3bRp\n9gLYjRvrY3McHVvQkpLI9+q660g0HDpEVpI//rDHnDktbQx32gcOpMLbnPY+OtqzkHKdOvY3flwc\nidpGjSjBS+/etNwpEG+4wW6hc7rG+WL0aDpvDz6o/890jzSpX1+LQT4/bMlkkWu64SUnexdtTswY\nL/N4TtGmFIkKjkEESDR4Ew5cnJ2Hxtn3iUUbW2AzMtxF22mn0baffkq/TfPmtOyf/7Rv17u3Lodh\nWvo2bXK3cJuFxNatA1580XObdevodzUtkXFxJL5YRJqF5DlTrLMQ/RVXkFBi8eRk40Y6fxxzaFpG\njx2jc3PwoPec+W4jdIcPkwvr0aNkDa9dW9+zboMQzJYt9P1atKDtBg2i78/ilK3jvA8zI+jWrXRP\nxsYCu3ejWzcXA3t56hcK1YLYWoUeoq1BA/rLzKRLwl/R5mTYMJr6UxaiQQNtkXOLIhD3SEEQBP8Q\n0VZROnYEpk/Xqformx49dOwOC6fYWHKjNAWMUtRp58QfZif/jDPobXvWWXpZw4b2OC3ev2mFY1JT\n6Xiffqr9XViEJCUBDz1EsWhmrueSEnvMnZto69pVizbejxnBDugaaEuXesZKmeJs2DDqfAN2IXrd\ndRTbZGYHdAouTv4CeMbJtWlD561WLRJJr79OcVtuWUdNl1kz1o+niYn2JBxJSTRYMGOG95gq7mV5\nEzbbtnmWVwB8Z0V9911dhJuH1P/2N/qN5s6ljvuePfRbsFAzyzUwXboAl15K5+bbb+lccUYDZ43D\nDh1ooMN5fW3aZO9JctFxDsbxxoAB5CY5dCiJqZEjgX//m9qZnq7bnZlJsZL9+lHMHeB5jd10E93P\nU6bQ57w8EkbbtpGLIw+E7NtHyzkGkfnjDxK+pkuniVsZhhdeoGQxr75Koq1DB91b9iXa2Pp66ql6\n2dKl2tKWm0sWRm6zKdoyMug3Sk2l9YWFVDqCXTjnz6dr/OKLtTVPCBtiUYijoHdCkyZk5K9Xj+ZX\nr6ZL1x/RZjpwXH45jRFywltvBv9+/fR4XqdO+vUYG0sGafNV5VZzUBAEQfBERFu4YVqrTJcu5q67\nPFOwOzF8c8tFo0b0Fr78cp0f3BRtjHPY3uy4mwJr0iSynDRurN0YzWq/Jl9+SUWqTzrJs8fhFIKc\nVdO0Mk2Y4LlvtsgB1DGfNk1//u47+/l0HvOGG2j/Zp50/l1M0cb+QTysXL++Z3xTfDzt57rrdOF1\nJ/370/TgQc/4vpYtPS1tDP8uZiZHzqBkVlJm0damDcUHfvghlZQoLiYXRacb6YQJZM35809KotKo\nkXaFNHt7LVvScu7JtW9P54Dd+ZhNm+yWr6FDSfA3b05t8xazeMUV9iLk7dpp65xZMD0ri7Jprlih\nyxA4LW1t2lAPd/p0+t0SEshv8KKLyMWRa7yMHk3n1VnbceNG+n06dybRWquWPZjHKdp++UVb37Ky\nSEx26aLbFaho+/VXbWlbuJDONbeRRVt+PgnYNm2oF79rF4nPRx+laz4/nxK0FBbSIJH0rMOOWkWF\nKFL0vDn3XHKuUIoeYZzUNFDRNm6cPaeWm6VtxgzykOZw0j59tGiLiaFLjCumpKR4f9QLgiAIdkS0\nhRsclNCpk/sw53/+oy1MTt55h1LYm9HjgcCWOLd4NNM9knnmGapvxriJtrQ0imm6805tFQS8v8kT\nErRwceIUbfzZtLSZx2BuuYU640OGUEefXSwBGhK+4gotgJy5sE3+/nfgxhu1WDV9gfj89OxJwuPd\nd7Xr2mWX0dS0gnirOXbSSTQ9eJCsYGZZgJYtyfq4dy+1wRzO5vabcYUffUR/rVvrNrPVqHFj6lnV\nrq2T4XTv7inaEhJom/bt9THefpt+T7Owc1QUuUHyoINZnmLyZC1QcnI8RRQL4pYtPa1ijDPbaUyM\n7m2a8W+ZmToWjROYOI8HUGzaFVfoXu1nn2kLt+lay8lY+HdRiixtBw/Sb960KbXDvPaNYGkAZF19\n7TWa37aNhFjv3vpa3b2besLOchIAWaYbNNDurADF45mxezt36rbn5NB3+ec/SQz/7W/0/Xft0ta4\n3bvpObJ1K9UE3LzZPz84oXpRWIji2iTazEdBWpr2eg3UPdL0UgbcLwt+3PLl5BRtgH5NuHl5C4Ig\nCO5Ice1w46yzaFTdLFbsL84kGIEydSplOnTDzdKWmEgxWpMmUWfWHLJlkTBqlF5mvsHL00l0ikmn\npS0+3l1wRkV51tNKT7fH82zaVHbmP+54cyyS0yUQoN4TuyLefDMJhxkzSOAOGaK3q1uXzoczop8t\ncN260b5MK+KgQVQy4YUXPF0++fcxxVKjRtrq2qsXWZU++og+N25MVrnp07V1q1MnEmimuHRzu0xL\nc485A7TFxxSlY8bQdc2CLjXVexIg8/c79VT6TcaPt7sidu1KOcxZ7M2fT2I7KYksWWbB8fh4Le6/\n+05/twYNtGvmtdeWnaHj0UdJLM2fT6KstOA1OnaknqrZ8/31Vz3PNfKYzz+n6Ykn0rmtXZtE5htv\nkMCKiiJByedq+3a6znhQoH59Eo3OeDzz+v6//6Pra/hw2ufs2STOWJDv3k3f/ayzPLOdCuHD0aNA\ndAxwzB43Zgo1b2MgJvxYTkz0NLjyY7pBA520lkUbG4F79dKhpNwOt9eFIAiC4BuxtIUbDRoAixeX\nHeNTGSjlWqwXgPe3MA/NNm1qj/vr3586uE8+qZeZVjC3kgNl4c09MinJHt/nBtcYYxIS7BbJhg09\nh5m9wd/T1/EAElhr11JH+6mnPNOouVnbmjShRChffEGfuRd05ZWU2IPrhzjFlJnd8+yzdWp6JiFB\np+EHtPXJzAEfHU0ukFOnkkjq1UtbCf2FLVLsLsqYVuPUVFrv3AbQou3vf6f7YOVK+t5NmtC9cc45\nZFVq3Zquudq1SYy0akW9ybVryYLEAtnstZ5zjnvOe/bzAuzC2rnNk0+SYGRRlphIYu/tt7VoO+88\niiWbM4cEkXOwgEVcv34kNBs10tWMf/yRrJWDB1Pcm1LkutqyJW3/3XeUOMaM12T++MN+feXn6+/V\nvTsdl+sQ7txJFtsqrEUjBIFatVBcl56JTkub27w3uneny9fN655FW/369Djr00fvc8IEuiW7ddOP\nKZ7y//kKtRUEQQgmgwYNwoEDB3DgwAFMNQwf8+fPx8Vu700Ho0aNQps2bdCrVy907twZ48ePP75u\n4MCB6Nev3/HPy5cvx0Cz7xAkRLQJwYFH+p0umwkJ9OdmdTrzTLu7Ib/BGze2Cyh/caYhY9EWF0f7\nLEtEBYuXXqKEKaefTpbETz4p337cRGLjxmQBMb/LgQOULVMpXQLBaVE0Rdv335NIdGKm1WcBXLcu\n1WRj62DPnsBtt5G16rffAreIPv00uXQ6Bam5Hzd3RYbFvFOgK0UufY88opdFR+vv1L49iUAWJiNH\n0jJ/MocOGUIuvN9+S0l8nDRurNvTqZMuFZCYSKLQzLZ64400vewyGrQw4yeZzp11wprUVM9869u3\n6yL0gD6X55xD39l0SzW54AJ7TTx2leUBIM7iuWoVWWn8yZ4rVF/mzMHLQ8jd2Xw08qPhtNP8y9yY\nkkKeueec47mOH/fx8eQ4sGKFfqRfdRWNIcTGerpHcvipiDZBEELFvHnzkJSU5CHaAmHSpElYtWoV\nVq1ahbfffhsZGRnH1+3ZswdfOeP0g4yINiE4nHsuxcm4FeIZMMBecsAbZ51F+ymrsLE3nEKPXQSb\nNqWshqGqHNyzJ8WG1asHvPkmuaOVh7FjdS04gFwl3YRnYqLuFbH7pDPbnz/+SEpR5kRn1sORIz0T\ngPiK7fNFTIw9/ooxRZsvscAZSd2yj157rd0qBmhrUbt29sQ9/9/e3cdWVd9xHH9/KbWVB586h5Pi\nKFCCoNBJwyjQpQGNPCxjTogPlfEH8SHZRGTpglmzxAUMRp2byTIlk+CWZZq5mRH/WRzUrDoFy1AH\nI45iXMTIgPoA/DGg87c/fr8jp9d729ueS++57eeV3PSec0/bX7+99/zO9/yeZs3yY7vyOXFfdJHv\n4nvDDT0nl4nEJ3eJj2OMJ5833eQTxptv7pkoRl0wwbeqrV/vuytGMv/fURfj+PIXmcl9U5N/H2TG\ncfx4//MmTPD/v+j/PGOG/99H4+Ciz1/875KSlK37YzQh7ubNvX/v3Xf3/TGPt7T1ZtIk31khuncR\nrZBz5529f5+ISD4eeeQRngjDMu6//34WhtnRd+7cSXNzM+AX4D5+/DgbNmzg0KFD1NXV0RLmXTh1\n6hQrVqxg2rRpNDc343Kt0xr8N1yLjI5di7S0tLAp2w3xAtKYNimMMWP8OlPZ5HvnYcYM35pRKFOn\n+oEVkydnbyFJu7lzfevQ+vV+O58JZKKkLd6iAj1b2nqzZUv/ylgoZWV+iYdLL83evS8SdR/Mtc5f\npmjM34UXnkvaqqp8It+f9fkimYnM5Mk9Fw6PJ3XxJTXKys51DX3rLd/8sGpVzwStttYvWh43fXrP\nz8SqVb61Mj7RSGar5YgRvotmS4sf/xaJmkoWLPCfi3ir5ZQpPRdvB7W0DQHRKSOaUBb8W+qzz/ru\nzPDkk/7Rmyhpizo15DJxYs/hubW1udeuF5ESt25dz/VSC6Guzo/Xz6GxsZHHHnuMtWvX0tHRwenT\npzl79izt7e18I+P6b/Pmzezbt483Qxlffvll9u7dy/79+7nyyiuZP38+r776KguiJaZiWlpa2Lhx\nI52dnaxdu5Yvx26sNjQ08MILL9DW1sbY8zQtrlraZGibMmVgXS3TorLSf80c75ZL1BITzakdufpq\n38qVbZxYWjz0kE80eru9HyVt2SaUySa6pX/ddeeStlmzBv6eqKnxNyeiKfzb23uOy4ySxObm3H3P\nqqr8Ve7tt/fcH80mGrdpk79y3rXLtwxmuzGSK/mM1mqLREtEPPXUuUXrI5l938yyd2mWkhL1so0m\nCYkU6pQYXZdo2n4RKabZs2ezZ88eTpw4QUVFBQ0NDXR0dNDe3k5j5nq7WcyZM4fq6mpGjBhBXV0d\n70XL8GSIukceOXKEHTt28LdoFuqgtbWVjfFrggJTS5uUvoMH/eCJoWjUKN/SFpr3+2SW/Tb6rFk+\n4cm2KHcpWbnSz2CabdH3bK6/3k/CMXWqnw20oiLZBBtmvhvwunV+TF/mTA4jR/pmjXyuYleu9Atq\n797tk6j4bKWRUaN6LvYOfoxhRYVvuli7tuei9XGNjX6x7ltu8V01o+aQbGVbtqzn+LoJE87dMJCS\nFQ01zkzaCqWiwj/6amkTkWGklxax86W8vJyamhq2bdvGvHnzmDlzJm1tbXR2dnJ1tjWNM1TEboyX\nlZXR3d3d6/FjxoyhqamJV155hXnRzWFg4cKFtLa28no0fr7ASvwKTgTfmhafyn4oMftil7l8vieb\nUk/YwHdz7W+/qqjLYkWFbxnLleT0x5gx5wYHZerPPOYPPui7sl5xRe71FTOtXu2/OucnusmVIN5x\nB9x4Y34T8DQ1+ffNAw/48ZAbNuRXFkm16Folmp/ofJg0qefKIyIixdDY2Mijjz7K1q1bufbaa1m/\nfj2zZ8/GMq6Jxo4dy8mTJxP9ru7ubnbt2sW99977hddaW1u55557mHQexoUPgas4EZE8RWP+0mTc\nON9qFjWL5Mus9xY9s/xnTB092rfQypBSXT2wt1Z/vPaaGmVFpPgaGxvZtGkTDQ0NjB49msrKyqxd\nI6uqqpg/fz7XXHMNS5YsYVk/JqmLxrSdOXOGRYsW8Z0sE80tXbqUy+PrDheQ9TVDyvlSX1/vOjo6\nivK7RURkcJnZHudcfd9HCqiOFJHSceDAgby6IUr2WOVbP2oiEhERERERkRRT0iYiIiIiIpJieSVt\nZrbYzN4xs04z+8IIdTOrMLPnwuu7zGxioQsqIiIiIiIyHPWZtJlZGfALYAkwHbjNzKZnHLYG+Ng5\nNwV4HHi40AUVEREREZH0KdYcGaUkaYzyaWmbA3Q65951zp0BngWWZxyzHHgmPH8eWGSZc2yKiIiI\niMiQUllZSVdXlxK3Xjjn6OrqojLBdLv5TPk/Hng/tn0Y+HquY5xz3Wb2KVAFHB9wyUREREREJNWq\nq6s5fPgwx44dK3ZRUq2yspLq6uoBf/+grtNmZncBdwFcddVVg/mrRURERESkwMrLy6mpqSl2MYa8\nfLpHfgBMiG1Xh31ZjzGzkcDFQFfmD3LObXHO1Tvn6s/XwnMiIiIiIiJDST5J2xtArZnVmNkFwK3A\n9oxjtgOrw/MVwE6njq0iIiIiIiKJ9dk9MoxR+z7wZ6AM2Oqc229mPwE6nHPbgaeB35hZJ/ARPrET\nERERERGRhKxYDWJmdgz4d8If8yU02UlSimFyimEyil9ypRDDrzrn1C8+T6ojU0MxTEbxS04xTC7t\nMcyrfixa0lYIZtbhnKsvdjlKmWKYnGKYjOKXnGIo2eh9kZximIzil5ximNxQiWE+Y9pERERERESk\nSJS0iYiIiIiIpFipJ21bil2AIUAxTE4xTEbxS04xlGz0vkhOMUxG8UtOMUxuSMSwpMe0iYiIiIiI\nDHWl3tImIiIiIiIypJVs0mZmi83sHTPrNLMNxS5PWpnZVjM7amb7YvsuM7OXzOxg+Hpp2G9m9kSI\n6dtmdl3xSp4OZjbBzNrM7J9mtt/M7gv7FcM8mVmlme02s7dCDB8M+2vMbFeI1XNmdkHYXxG2O8Pr\nE4tZ/rQwszIz22tmL4ZtxU9yUh3ZN9WPyamOTEb1Y+EMhzqyJJM2MysDfgEsAaYDt5nZ9OKWKrW2\nAYsz9m0AdjjnaoEdYRt8PGvD4y7gl4NUxjTrBn7gnJsOzAW+F95rimH+TgMLnXOzgDpgsZnNBR4G\nHnfOTQE+BtaE49cAH4f9j4fjBO4DDsS2FT/JSnVk3rah+jEp1ZHJqH4snCFfR5Zk0gbMATqdc+86\n584AzwLLi1ymVHLO/RX4KGP3cuCZ8PwZ4Nux/b923uvAJWb2lcEpaTo55z50zv09PD+JPyGMRzHM\nW4jFqbBZHh4OWAg8H/ZnxjCK7fPAIjOzQSpuKplZNbAM+FXYNhQ/yU11ZB5UPyanOjIZ1Y+FMVzq\nyFJN2sYD78e2D4d9kp9xzrkPw/MjwLjwXHHtRWhC/xqwC8WwX0K3hTeBo8BLwCHgE+dcdzgkHqfP\nYxhe/xSoGtwSp87PgB8Cn4XtKhQ/yU3noYHTuX2AVEcOjOrHghgWdWSpJm1SIM5PH6opRPtgZmOA\nPwDrnHMn4q8phn1zzv3POVcHVONbAaYVuUglw8y+CRx1zu0pdllEhhOd2/OnOnLgVD8mM5zqyFJN\n2j4AJsS2q8M+yc9/ou4I4evRsF9xzcLMyvGV0W+dc38MuxXDAXDOfQK0AQ34bjEjw0vxOH0ew/D6\nxUDXIBc1TeYD3zKz9/Dd3BYCP0fxk9x0Hho4ndv7SXVkYah+HLBhU0eWatL2BlAbZoa5ALgV2F7k\nMpWS7cDq8Hw18KfY/u+G2Z3mAp/GujcMS6Gf89PAAefcT2MvKYZ5MrPLzeyS8PxC4Ab8uIc2YEU4\nLDOGUWxXADvdMF5Q0jn3gHOu2jk3EX+u2+mca0bxk9xURw6czu39oDoyGdWPyQ2rOtI5V5IPYCnw\nL3zf3x8VuzxpfQC/Az4EzuL79K7B993dARwE/gJcFo41/Ixjh4B/APXFLn+xH8ACfLeOt4E3w2Op\nYtivGM4E9oYY7gN+HPZPAnYDncDvgYqwvzJsd4bXJxX7b0jLA2gCXlT89MjjvaI6su8YqX5MHkPV\nkcnip/qxsPEc0nWkhT9AREREREREUqhUu0eKiIiIiIgMC0raREREREREUkxJm4iIiIiISIopaRMR\nEREREUkxJW0iIiIiIiIppqRNREREREQkxZS0iYiIiIiIpJiSNhERERERkRT7P+EhuIYutR+VAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aee4240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('loss, per batch')\n",
    "plt.plot(bl_noBN.log_values['loss'], 'b-', label='no BN');\n",
    "plt.plot(bl_BN.log_values['loss'], 'r-', label='with BN');\n",
    "plt.legend(loc='upper right')\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('accuracy, per batch')\n",
    "plt.plot(bl_noBN.log_values['acc'], 'b-', label='no BN');\n",
    "plt.plot(bl_BN.log_values['acc'], 'r-', label='with BN');\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relu activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_3 (None, 784) []\n",
      "dense_13 (None, 128) [100352, 128]\n",
      "dense_14 (None, 128) [16384, 128]\n",
      "dense_15 (None, 128) [16384, 128]\n",
      "dense_16 (None, 128) [16384, 128]\n",
      "dense_17 (None, 128) [16384, 128]\n",
      "dense_18 (None, 10) [1280, 10]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/local/conda/lib/python3.6/site-packages/ipykernel_launcher.py:13: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 55000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "55000/55000 [==============================] - 2s - loss: 0.3187 - acc: 0.9047 - val_loss: 0.1443 - val_acc: 0.9555\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11c6878d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see http://cs231n.github.io/neural-networks-3/\n",
    "\n",
    "model = mlp(False, 'relu')\n",
    "print_layers(model)\n",
    "\n",
    "bl_noBN = BatchLogger()\n",
    "\n",
    "from keras.optimizers import Adam\n",
    "model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(mnist.train.images, mnist.train.labels,\n",
    "          batch_size=128, epochs=1, verbose=1, callbacks=[bl_noBN],\n",
    "          validation_data=(mnist.test.images, mnist.test.labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_4 (None, 784) []\n",
      "dense_19 (None, 128) [100352, 128]\n",
      "batch_normalization_6 (None, 128) [128, 128, 128, 128]\n",
      "dense_20 (None, 128) [16384, 128]\n",
      "batch_normalization_7 (None, 128) [128, 128, 128, 128]\n",
      "dense_21 (None, 128) [16384, 128]\n",
      "batch_normalization_8 (None, 128) [128, 128, 128, 128]\n",
      "dense_22 (None, 128) [16384, 128]\n",
      "batch_normalization_9 (None, 128) [128, 128, 128, 128]\n",
      "dense_23 (None, 128) [16384, 128]\n",
      "batch_normalization_10 (None, 128) [128, 128, 128, 128]\n",
      "dense_24 (None, 10) [1280, 10]\n",
      "Train on 55000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "55000/55000 [==============================] - 6s - loss: 0.2868 - acc: 0.9118 - val_loss: 0.1287 - val_acc: 0.9604\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11b7feda0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see http://cs231n.github.io/neural-networks-3/\n",
    "\n",
    "model = mlp(True, 'relu')\n",
    "print_layers(model)\n",
    "\n",
    "bl_BN = BatchLogger()\n",
    "\n",
    "from keras.optimizers import Adam\n",
    "model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(mnist.train.images, mnist.train.labels,\n",
    "          batch_size=128, epochs=1, verbose=1, callbacks=[bl_BN],\n",
    "          validation_data=(mnist.test.images, mnist.test.labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5g+t/6EzOY61PNwBq8gqIoIDzWrtcLE/id+vzD5wLKFFyEx8wn9OpXukMIBOV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12019f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('loss, per batch')\n",
    "plt.plot(bl_noBN.log_values['loss'], 'b-', label='no BN');\n",
    "plt.plot(bl_BN.log_values['loss'], 'r-', label='with BN');\n",
    "plt.legend(loc='upper right')\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('accuracy, per batch')\n",
    "plt.plot(bl_noBN.log_values['acc'], 'b-', label='no BN');\n",
    "plt.plot(bl_BN.log_values['acc'], 'r-', label='with BN');\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
